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REVIEW 3 major objections 4 minor 42 references

Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper proposes that multimodal foundation AI, kept in check by fisheries experts, can automate salmon species identification, counting, and length measurement at remote river monitoring sites.

desk verdict A clearly written project overview with no experimental results; the central efficiency claim hangs on an unmeasured VLM-routing assumption. read the letter →

arxiv 2505.06637 v1 pith:OVOH4RGJ submitted 2025-05-10 cs.AI

Chi Xu , Yili Jin , Sami Ma , Rongsheng Qian , Hao Fang , Jiangchuan Liu , Xue Liu , Edith C.H. Ngai
show 3 more authors
William I. Atlas Katrina M. Connors Mark A. Spoljaric
This is my paper · ORCID
classification cs.AI
keywords multimodalfoundationAIvision-languagemodelsexpert-in-the-loopsalmonmonitoringsonar-basedfishcountingfisheriesmanagementIndigenousdatasovereigntyedge-clouddeployment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes that multimodal foundation AI—particularly vision-language models that can describe images in words—can be adapted, with fisheries biologists kept in the loop, to automate the identification, counting, and length measurement of wild salmon at remote river weirs and sonar sites. The authors build on existing video and sonar monitoring systems and argue that routing low-confidence detections through a vision-language model, and only sending uncertain cases to human experts, will cut manual review effort while improving accuracy. Sonar analysis would be upgraded by fusing sonar frames with echograms in a foundation model whose outputs feed tracking, counting, and length estimates. The paper is an architecture and implementation plan: it specifies workflows, evaluation metrics, and a co-development process with Indigenous stewardship partners, and it acknowledges in its assumptions section that performance across sites remains uncertain. It reports no field measurements yet, but if the plan works, monitoring sites that currently depend on preseason forecasts and labor-intensive frame review could shift to adaptive in-season decisions.

What carries the argument

The load-bearing mechanism is the expert-in-the-loop verification loop: a lightweight detector/classifier on site flags low-confidence frames, a vision-language model (a model trained to answer questions about images in natural language) classifies them with explanations, and only uncertain cases are escalated to fisheries experts, whose corrections update the model. A second mechanism is multimodal sonar fusion: sonar frames and echograms (time-versus-height sonar returns) are encoded separately and merged through attention, with expert comments included as text, inside an adapted SAM2 foundation model for segmentation, tracking, counting, and length measurement. The design's core promise is that expert effort concentrates on the hardest cases while the model automates the rest, so accuracy rises as corrections accumulate.

What would settle it

Run the proposed pipeline for a full field season and compare, against expert labels, the vision-language model's species-classification accuracy on low-confidence frames, the total expert review hours per fish, and the mAP/F1 and MOTA/HOTA numbers against the YOLO/RT-DETR and CFC baselines named in the paper; if the VLM does not beat the base model on the low-confidence subset, or if expert hours do not decline while accuracy holds, the central efficiency claim is refuted.

Watch

Extended reading notes

Core claim

On its own terms, this paper's central claim is that a monitoring pipeline combining a base detector/classifier, a vision-language model, and expert review can make salmon monitoring accurate enough and cheap enough for in-season fisheries management in Indigenous rivers. For video, low-confidence detections from single-modality detectors are sent to a vision-language model, which produces an identification plus a textual explanation; if uncertainty remains, the frame and explanation are flagged for a fisheries biologist, and corrections are fed back as training data. For sonar, the paper proposes adapting the SAM2 foundation model to fuse sonar frames and echograms through separate encoders and attention-based fusion, with expert comments as optional text inputs, supporting detection, tracking, counting, and length measurement. The paper explicitly acknowledges in its Figure 3 that an off-the-shelf vision-language model misidentified a sockeye as a Chinook, and uses that as evidence that expert validation is necessary rather than optional. The claim is advanced as a project design: the expected result is stated, but no measurements are reported that would confirm it.

Load-bearing premise

The plan assumes that a pre-trained vision-language model, refined through expert feedback, will classify salmon species accurately enough—and the routing will be selective enough—that the number of frames needing expert review actually falls instead of staying about the same.

Editorial extensions

If this is right

  • If the vision-language model reduces the fraction of frames needing expert review, weir-based monitoring shifts from semi-automated to largely AI-driven, lowering the manual-labour bottleneck that currently limits in-season data delivery.
  • Sonar-based monitoring can extend counting and length measurement across the full river width without building weirs, opening data collection for sites that currently lack infrastructure.
  • Synchronizing sonar and video where both are available should improve tracking and counting through early fusion, compared with either modality alone.
  • Open-sourcing datasets and models, together with federated learning, could spread the system across multiple Indigenous territories while keeping raw data under community control.
  • Reliable in-season abundance estimates would let managers respond to actual returns rather than relying on preseason forecasts, supporting selective harvest of healthy stocks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A test the paper leaves implicit: measure expert review hours per thousand fish before and after VLM routing is added; if the VLM is not clearly better than the base model on low-confidence frames, the loop could add workload rather than remove it.
  • The paper's own Figure 3 hints that spawning-phase and rare-species identifications stay hard for off-the-shelf vision-language models; a plausible outcome is that expert workload drops for common species but stays high for the rare, data-poor cases that matter most to conservation.
  • The evaluation metrics listed in the paper do not set a numeric threshold for what fisheries management standards require; until such a threshold is defined, a reader cannot tell what accuracy would justify a management decision.
  • The claim that VLM-assisted annotation reduces errors by inexperienced annotators can be tested directly by comparing inter-annotator agreement with and without VLM-generated suggestions.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper proposes an expert-in-the-loop multimodal foundation AI framework for wild salmon monitoring in Indigenous rivers of the Pacific Northwest. It integrates underwater video from counting weirs and sonar units for automated species identification, counting, and length measurement. The proposed pipeline routes low-confidence video frames to a vision-language model (VLM) for refinement, with expert review for remaining uncertain cases, and adapts foundation models such as SAM2 for sonar processing with echogram fusion. The manuscript includes problem motivation, alignment with Sustainable Development Goals, a system architecture, planned evaluation metrics, expected impacts, risk discussion, and a project team description. No experimental results are reported; Sections 5 and 6 present evaluation criteria and expected outcomes, not measurements.

Significance. If the proposed framework works as described, it would address a genuine and pressing need: monitoring data limitations for salmon stocks in remote, roadless rivers, with direct benefits for Indigenous-led conservation and adaptive fisheries management. The interdisciplinary team and existing relationships with Indigenous communities, as well as the commitment to open data and culturally informed co-development, are notable strengths that give the proposal practical credibility. However, the paper's central claims of reduced manual effort, expedited results, and improved accuracy are not validated by any quantitative evidence. The only concrete data point (Figure 3) shows an off-the-shelf VLM misclassifying a sockeye as a Chinook, which illustrates the need for expert review but does not demonstrate that the proposed refinement loop will be accurate or efficient. The paper is a project proposal rather than a completed research study; its value depends on future implementation and evaluation.

major comments (3)
  1. [Abstract and Section 4.2] The abstract claims 'reducing manual effort, expediting delivery of results, and improving decision-making accuracy,' but the manuscript provides no experimental evidence for any of these outcomes. Section 5 only lists planned evaluation criteria, and Section 7 explicitly states that performance 'remains uncertain' and mentions only 'preliminary results from the Yakoun River' without reporting numbers. Figure 3, the sole empirical illustration, shows a VLM error that requires expert correction. This is a load-bearing gap: if VLM accuracy on low-confidence frames remains low, routing frames through a VLM could add an extra review layer rather than reduce expert workload. The authors must either provide quantitative results from their deployments or substantially reframe the paper as a proposal and temper the achievement-oriented claims.
  2. [Section 4.2, Paragraph 3] The statement that 'as expert-reviewed frames accumulate, the refined VLM progressively improves its performance, reducing reliance on manual verification over time' is an unsupported assertion. The manuscript does not specify how expert corrections are incorporated (e.g., fine-tuning, in-context learning, or retrieval augmentation), nor does it provide learning curves, error-rate measurements, or workload statistics. Without a concrete refinement mechanism and evidence of its effect, the claimed efficiency gain is speculative and cannot be assessed by the reader.
  3. [Section 4.3 and Figure 8] The sonar processing pipeline, which adapts SAM2 with echogram fusion and uses 'attention-based feature rearrangement' for length measurement, is described only at a high level. No results are reported for detection, tracking, counting, or length estimation, despite Section 5.2 defining appropriate metrics. Since the paper promises a full pipeline including length measurement, this missing validation is central to the claimed contribution. The authors should report at least preliminary results on a defined sonar dataset, or clearly mark these components as untested future work.
minor comments (4)
  1. [Figure 3 captions] The text 'Vison Language Model' appears twice in Figure 3 captions; this should be 'Vision Language Model' or, more precisely, 'vision-language model.'
  2. [Section 3.1] The model name 'LLaV A' has a formatting issue; it should be 'LLaVA.'
  3. [Section 7] The phrase 'preliminary results from the Yakoun River suggest that automated model analysis is not only feasible but also critical' is vague: no data, metrics, or study details are provided. Please either include the relevant results in a data section or remove the unverifiable claim.
  4. [General] The 'Project Team Description' section is unusual for a research paper and reads like grant-proposal material. Consider moving this content to supplementary material or an acknowledgment-style appendix if the venue permits.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a proposal with no fitted-parameter derivations, no in-paper predictions that reduce to their inputs, and no load-bearing self-citation chain.

full rationale

This manuscript is a project and implementation proposal rather than a derivation. It contains no fitted parameters, no computed predictions, and no theorem whose conclusion is assumed as an input. The central claim that VLM-plus-expert refinement will reduce manual effort rests on an unverified assumption, not on a circular equation; Section 7 explicitly acknowledges that 'the performance of such AI models for automated detections, tracking, counting, and length measurement across different sites remains uncertain.' Figure 3 is an honest failure case showing an off-the-shelf VLM misclassifying a sockeye as a Chinook, which directly undercuts any suggestion that VLM accuracy is presupposed by the design. Cited prior work (Atlas et al. 2023; Xu et al. 2024) is used as project lineage and deployment context, not as evidence that the proposed accuracy gains hold. The proposed evaluation in Section 5 compares against external baselines and expert-reviewed ground truth, so the planned assessment is externally falsifiable rather than definitionally tied to the method's own outputs. No circular step meeting the required standard is present.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No new physical or mathematical entities are introduced. The paper relies entirely on domain assumptions about model performance and deployment feasibility, which are stated but not validated.

assumptions (3)
  • domain assumption Pre-trained vision-language models (for example LLaVA, gpt-4o, o1) can provide sufficiently accurate salmon species identification for low-confidence frames when supplemented by expert review.
    Invoked in Sections 3.1 and 4.2 as the basis for routing low-confidence detections to a VLM instead of directly to experts; the only demonstrated example is a VLM error.
  • domain assumption Fusing sonar frames with echograms through attention-based multimodal encoders improves detection, tracking, counting, and length measurement relative to single-modality sonar.
    Proposed in Section 4.3 as the core sonar innovation; no experimental evidence is presented for the fusion benefit.
  • domain assumption Expert-in-the-loop annotation and continual learning will improve model generalization across different Indigenous river sites.
    This is the design premise of Sections 4.1 and 3.2; the paper acknowledges in Section 7 that cross-site performance remains uncertain.

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Cite this review

Pith. "Pith review of Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers." pith.science (2026). https://pith.science/paper/OVOH4RGJ

@misc{pith2026250506637,
  author       = {Pith},
  title        = {Pith review of: Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OVOH4RGJ}},
  note         = {Machine review of arXiv:2505.06637}
}
read the original abstract

Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Yet climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This project explores the integration of multimodal foundation AI and expert-in-the-loop frameworks to enhance wild salmon monitoring and sustainable fisheries management in Indigenous rivers across Pacific Northwest. By leveraging video and sonar-based monitoring, we develop AI-powered tools for automated species identification, counting, and length measurement, reducing manual effort, expediting delivery of results, and improving decision-making accuracy. Expert validation and active learning frameworks ensure ecological relevance while reducing annotation burdens. To address unique technical and societal challenges, we bring together a cross-domain, interdisciplinary team of university researchers, fisheries biologists, Indigenous stewardship practitioners, government agencies, and conservation organizations. Through these collaborations, our research fosters ethical AI co-development, open data sharing, and culturally informed fisheries management.

Figures

Figures reproduced from arXiv: 2505.06637 by the authors.

Figure 1
Figure 1. (a) A salmon counting weir at Koeye River (in Heiltsuk First Nation’s traditional territory, northern British Columbia) with salmon swimming passing the fish channel, (b) sample underwater video frames with salmon appearances, (c) object segmentation with species identification. (a) (b) (c) (d) [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. (a) A mounted ARIS sonar camera, (b) sonar deployment in the Yakoun River, Haida Nation’s traditional territory, (c) sample frames from ARIS sonar, (d) salmon detection and tracking in sonar frames. the newly developed vision foundation model [Achiam et al., 2023; Liu et al., 2024a] also helps improve performance and generate timely and accurate insights. For effective AI system deployment, ensuring reliable data ca… view at source ↗
Figure 3
Figure 3. An off-the-shelf vision language model (e.g., OpenAI o1) can identify some cases but also makes errors, [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: This project supports SalmonVision & Selective Fishery [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Vision language model verification and refinement. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 7
Figure 7. Figure 7: Sonar frames and echogram, as two different modalities. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Multimodal foundation model for sonar domain. [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

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Works this paper leans on

42 extracted references · 34 canonical work pages

  1. [1]

    Gpt-4 technical report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 , 2023

    arXiv 2023

  2. [2]

    Indigenous systems of management for culturally and ecologically resilient pacific salmon (oncorhynchus spp.) fisheries

    William I Atlas, Natalie C Ban, Jonathan W Moore, Adrian M Tuohy, Spencer Greening, Andrea J Reid, Nicole Morven, Elroy White, William G Housty, Jess A Housty, et al. Indigenous systems of management for culturally and ecologically resilient pacific salmon (oncorhynchus spp.) fisheries. BioScience , 71(2):186--204, 2021

    work page 2021

  3. [3]

    Wild salmon enumeration and monitoring using deep learning empowered detection and tracking

    William I Atlas, Sami Ma, Yi Ching Chou, Katrina Connors, Daniel Scurfield, Brandon Nam, Xiaoqiang Ma, Mark Cleveland, Janvier Doire, Jonathan W Moore, et al. Wild salmon enumeration and monitoring using deep learning empowered detection and tracking. Frontiers in Marine Science , 10:1200408, 2023

    work page 2023

  4. [4]

    Evaluating multiple object tracking performance: The clear mot metrics

    Keni Bernardin and Rainer Stiefelhagen. Evaluating multiple object tracking performance: The clear mot metrics. EURASIP Journal on Image and Video Processing , 2008:1--10, 2008

    work page 2008

  5. [5]

    Indigenous peoples and salmon stewardship: a critical relationship

    Courtney Carothers, Jessica Black, Stephen J Langdon, Rachel Donkersloot, Danielle Ringer, Jesse Coleman, Erika R Gavenus, Wilson Justin, Mike Williams, Freddie Christiansen, et al. Indigenous peoples and salmon stewardship: a critical relationship. 2021

    work page 2021

  6. [6]

    Masked and shuffled blind spot denoising for real-world images

    Hamadi Chihaoui and Paolo Favaro. Masked and shuffled blind spot denoising for real-world images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 3025--3034, 2024

    work page 2024

  7. [7]

    Class-balanced loss based on effective number of samples

    Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie. Class-balanced loss based on effective number of samples. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 9268--9277, 2019

    work page 2019

  8. [8]

    Multi-year persistence of the 2014/15 north pacific marine heatwave

    Emanuele Di Lorenzo and Nathan Mantua. Multi-year persistence of the 2014/15 north pacific marine heatwave. Nature Climate Change , 6(11):1042--1047, 2016

    work page 2014

Show all 42 references
  1. [9]

    Spatial and temporal patterns of covariation in productivity of chinook salmon populations of the northeastern pacific ocean

    Brigitte Dorner, Matthew J Catalano, and Randall M Peterman. Spatial and temporal patterns of covariation in productivity of chinook salmon populations of the northeastern pacific ocean. Canadian Journal of Fisheries and Aquatic Sciences , 75(7):1082--1095, 2018

    2018

  2. [10]

    The sociocultural significance of pacific salmon to tribes and first nations

    Earth Economies. The sociocultural significance of pacific salmon to tribes and first nations. Special Report to the Pacific Salmon Commission , 2021

    2021

  3. [11]

    o licher and Charlotte Laufk \

    Thomas L Fr \"o licher and Charlotte Laufk \"o tter. Emerging risks from marine heat waves. Nature communications , 9(1):650, 2018

    2018

  4. [12]

    Image restoration by denoising diffusion models with iteratively preconditioned guidance

    Tomer Garber and Tom Tirer. Image restoration by denoising diffusion models with iteratively preconditioned guidance. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 25245--25254, 2024

    2024

  5. [13]

    Deep learning , volume 196

    Ian Goodfellow. Deep learning , volume 196. MIT press, 2016

    2016

  6. [14]

    Killian, Catherine Ressijac, Peter Boucher, Andrew Davies, and Milind Tambe

    Lucia Gordon, Nikhil Behari, Samuel Collier, Elizabeth Bondi - Kelly, Jackson A. Killian, Catherine Ressijac, Peter Boucher, Andrew Davies, and Milind Tambe. Find rhinos without finding rhinos: Active learning with multimodal imagery of south african rhino habitats. In Proceed...

    2023

  7. [15]

    The caltech fish counting dataset: a benchmark for multiple-object tracking and counting

    Justin Kay, Peter Kulits, Suzanne Stathatos, Siqi Deng, Erik Young, Sara Beery, Grant Van Horn, and Pietro Perona. The caltech fish counting dataset: a benchmark for multiple-object tracking and counting. In European Conference on Computer Vision , pages 290--311. Springer, 2022

    2022

  8. [16]

    Align and distill: Unifying and improving domain adaptive object detection

    Justin Kay, Timm Haucke, Suzanne Stathatos, Siqi Deng, Erik Young, Pietro Perona, Sara Beery, and Grant Van Horn. Align and distill: Unifying and improving domain adaptive object detection. arXiv preprint arXiv:2403.12029 , 2024

    2024 arXiv

  9. [17]

    Fishnet: A large-scale dataset and benchmark for fish recognition, detection, and functional trait prediction

    Faizan Farooq Khan, Xiang Li, Andrew J Temple, and Mohamed Elhoseiny. Fishnet: A large-scale dataset and benchmark for fish recognition, detection, and functional trait prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 20496--20506, 2023

    2023

  10. [18]

    Changing central pacific el ni \ n os reduce stability of north american salmon survival rates

    D Patrick Kilduff, Emanuele Di Lorenzo, Louis W Botsford, and Steven LH Teo. Changing central pacific el ni \ n os reduce stability of north american salmon survival rates. Proceedings of the National Academy of Sciences , 112(35):10962--10966, 2015

    2015

  11. [19]

    Long-term monitoring of bird flocks in the wild

    Kshitiz, Sonu Shreshtha, Ramy Mounir, Mayank Vatsa, Richa Singh, Saket Anand, Sudeep Sarkar, and Sevaram Mali Parihar. Long-term monitoring of bird flocks in the wild. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI , pages ...

    2023

  12. [20]

    Fedvision: An online visual object detection platform powered by federated learning

    Yang Liu, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu, and Qiang Yang. Fedvision: An online visual object detection platform powered by federated learning. In Proceedings of the AAAI conference on artificial intelligence , volume...

    2020

  13. [21]

    Visual instruction tuning

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems , 36, 2024

    2024

  14. [22]

    Benchmarking fish dataset and evaluation metric in keypoint detection - towards precise fish morphological assessment in aquaculture breeding

    Weizhen Liu, Jiayu Tan, Guangyu Lan, Ao Li, Dongye Li, Le Zhao, Xiaohui Yuan, and Nanqing Dong. Benchmarking fish dataset and evaluation metric in keypoint detection - towards precise fish morphological assessment in aquaculture breeding. In Proceedings of the Thirty-Third Int...

    2024

  15. [23]

    Hota: A higher order metric for evaluating multi-object tracking

    Jonathon Luiten, Aljosa Osep, Patrick Dendorfer, Philip HS Torr, Laura Leal-Taixé, and Bastian Leibe. Hota: A higher order metric for evaluating multi-object tracking. International Journal of Computer Vision , 129(2):548--578, 2021

    2021

  16. [24]

    Leo satellite network access in the wild: Potentials, experiences, and challenges

    Sami Ma, Yi Ching Chou, Miao Zhang, Hao Fang, Haoyuan Zhao, Jiangchuan Liu, and William I Atlas. Leo satellite network access in the wild: Potentials, experiences, and challenges. IEEE Network , 2024

    2024

  17. [25]

    Conservation risks and portfolio effects in mixed-stock fisheries

    Jonathan W Moore, Brendan M Connors, and Emma E Hodgson. Conservation risks and portfolio effects in mixed-stock fisheries. Fish and Fisheries , 22(5):1024--1040, 2021

    2021

  18. [26]

    Learning to reason with llms, 2024

    OpenAI. Learning to reason with llms, 2024. [Accessed 19-Sep-2024]

    2024

  19. [27]

    Canada’s wild salmon policy: an assessment of conservation progress in british columbia

    Michael HH Price, Karl K English, Andrew G Rosenberger, Misty MacDuffee, and John D Reynolds. Canada’s wild salmon policy: an assessment of conservation progress in british columbia. Canadian Journal of Fisheries and Aquatic Sciences , 74(10):1507--1518, 2017

    2017

  20. [28]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning , p...

    2021

  21. [29]

    Sam 2: Segment anything in images and videos

    Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman R \"a dle, Chloe Rolland, Laura Gustafson, et al. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714 , 2024

    2024 arXiv

  22. [30]

    Performance measures and a data set for multi-target, multi-camera tracking

    Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, and Carlo Tomasi. Performance measures and a data set for multi-target, multi-camera tracking. In European Conference on Computer Vision (ECCV) , pages 17--35. Springer, 2016

    2016

  23. [31]

    Prediction, precaution, and policy under global change

    Daniel E Schindler and Ray Hilborn. Prediction, precaution, and policy under global change. Science , 347(6225):953--954, 2015

    2015

  24. [32]

    Relationships between pacific salmon and aquatic and terrestrial ecosystems: implications for ecosystem-based management

    Jessica C Walsh, Jane E Pendray, Sean C Godwin, Kyle A Artelle, Holly K Kindsvater, Rachel D Field, Jennifer N Harding, Noel R Swain, and John D Reynolds. Relationships between pacific salmon and aquatic and terrestrial ecosystems: implications for ecosystem-based management. ...

    2020

  25. [33]

    Report of the skeena independent science review panel

    Carl J Walters, JA Lichatowich, Randall M Peterman, and John D Reynolds. Report of the skeena independent science review panel. A report to the Canadian Department of Fisheries and Oceans and the British Columbia Ministry of the Environment , 15, 2008

    2008

  26. [34]

    Yolov10: Real-time end-to-end object detection

    Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding. Yolov10: Real-time end-to-end object detection. arXiv preprint arXiv:2405.14458 , 2024

    2024 arXiv

  27. [35]

    Evolutionary history of pacific salmon in dynamic environments

    Robin S Waples, George R Pess, and Tim Beechie. Evolutionary history of pacific salmon in dynamic environments. Evolutionary Applications , 1(2):189--206, 2008

    2008

  28. [36]

    Simple online and realtime tracking with a deep association metric

    Nicolai Wojke, Alex Bewley, and Dietrich Paulus. Simple online and realtime tracking with a deep association metric. In 2017 IEEE international conference on image processing (ICIP) , pages 3645--3649. IEEE, 2017

    2017

  29. [37]

    A survey of human-in-the-loop for machine learning

    Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, and Liang He. A survey of human-in-the-loop for machine learning. Future Generation Computer Systems , 135:364--381, 2022

    2022

  30. [38]

    Salina: Towards sustainable live sonar analytics in wild ecosystems

    Chi Xu, Rongsheng Qian, Hao Fang, Xiaoqiang Ma, William I Atlas, Jiangchuan Liu, and Mark A Spoljaric. Salina: Towards sustainable live sonar analytics in wild ecosystems. In Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems , pages 68--81, 2024

    2024

  31. [39]

    A history of salmon and people in the central valley region of california

    Ronald M Yoshiyama. A history of salmon and people in the central valley region of california. Reviews in Fisheries Science , 7(3-4):197--239, 1999

    1999

  32. [40]

    Detrs beat yolos on real-time object detection

    Yian Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang, Qingqing Dang, Yi Liu, and Jie Chen. Detrs beat yolos on real-time object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 16965--16974, 2024

    2024

  33. [41]

    Evaluation of openai o1: Opportunities and challenges of agi

    Tianyang Zhong, Zhengliang Liu, Yi Pan, Yutong Zhang, Yifan Zhou, Shizhe Liang, Zihao Wu, Yanjun Lyu, Peng Shu, Xiaowei Yu, et al. Evaluation of openai o1: Opportunities and challenges of agi. arXiv preprint arXiv:2409.18486 , 2024

    2024

  34. [42]

    write newline

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