Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data | Paper Citation Record

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This paper cites an unresolved cited work.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Unresolved cited work

Reference 1

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This paper cites Snap happy: camera traps are an effective sampling tool when compared with alternative methods,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Snap happy: camera traps are an effective sampling tool when compared with alternative methods,

Reference 2

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This paper cites Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks,

Reference 3

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This paper cites Software to facilitate and streamline camera trap data management: A review,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Software to facilitate and streamline camera trap data management: A review,

Reference 4

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This paper cites Advances in image acquisition and processing technologies transforming animal ecological studies,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Advances in image acquisition and processing technologies transforming animal ecological studies,

Reference 5

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This paper cites Component processes of detection probability in camera - trap studies: understanding the occurrence of false-negatives,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Component processes of detection probability in camera - trap studies: understanding the occurrence of false-negatives,

Reference 6

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This paper cites Recommended guiding principles for reporting on camera trapping research,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Recommended guiding principles for reporting on camera trapping research,

Reference 7

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This paper cites You only look once: Unified, real -time object detection,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data You only look once: Unified, real -time object detection,

Reference 8

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This paper cites Best practices and software for the management and sharing of camera trap data for small and large scales studies,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Best practices and software for the management and sharing of camera trap data for small and large scales studies,

Reference 9

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This paper cites Snapshot Serengeti, high - frequency annotated camera trap images of 40 mammalian species in an African savanna,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Snapshot Serengeti, high - frequency annotated camera trap images of 40 mammalian species in an African savanna,

Reference 10

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This paper cites Planning for success: identifying effective and efficient survey designs for monitoring,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Planning for success: identifying effective and efficient survey designs for monitoring,

Reference 11

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This paper cites A novel method to reduce time investment when processing videos from camera trap studies,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data A novel method to reduce time investment when processing videos from camera trap studies,

Reference 12

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This paper cites R: a language for data analysis and graphics,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data R: a language for data analysis and graphics,

Reference 13

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This paper cites Efficient pipeline for camera trap image review. arXiv,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Efficient pipeline for camera trap image review. arXiv,

Reference 14

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This paper cites Fennell, C.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Fennell, C

Reference 15

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This paper cites Object detection in 20 years: A survey,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Object detection in 20 years: A survey,

Reference 16

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Observation a5615b0d-19ea-4f40-b6e3-eea205d7e741 · outbound

This paper cites Biodiversity studies: science and policy,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Biodiversity studies: science and policy,

Reference 17

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This paper cites Enhancing biodiversity conservation and monitoring in protected areas through efficient data management,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Enhancing biodiversity conservation and monitoring in protected areas through efficient data management,

Reference 18

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This paper cites Ecoinformatics: supporting ecology as a data -intensive science,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Ecoinformatics: supporting ecology as a data -intensive science,

Reference 19

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This paper cites Object detection with deep learning: A review,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Object detection with deep learning: A review,

Reference 20

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Observation 64ea47d9-5541-4844-b689-bd0e8c96486a · outbound

This paper cites Harnessing Artificial Intelligence for Wildlife Conservation.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Harnessing Artificial Intelligence for Wildlife Conservation

Reference 21

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This paper cites Empowering wildlife guardians: an equitable digital stewardship and reward system for biodiversity conservation using deep learning and 3/4G camera traps,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Empowering wildlife guardians: an equitable digital stewardship and reward system for biodiversity conservation using deep learning and 3/4G camera traps,

Reference 22

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This paper cites Deep learning object detection methods for ecological camera trap data,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Deep learning object detection methods for ecological camera trap data,

Reference 23

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This paper cites A comprehensive overview of technologies for species and habitat monitoring and conservation,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data A comprehensive overview of technologies for species and habitat monitoring and conservation,

Reference 24

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This paper cites A Survey on Multimodal Large Language Models.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data A Survey on Multimodal Large Language Models

Reference 25

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This paper cites Contextual object detection with multimodal large language models,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Contextual object detection with multimodal large language models,

Reference 26

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This paper cites Learning to prompt for vision-language models,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Learning to prompt for vision-language models,

Reference 27

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This paper cites Pre -trained language models and their applications,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Pre -trained language models and their applications,

Reference 28

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This paper cites Vcoder : Versatile vision encoders for multimodal large language models,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Vcoder : Versatile vision encoders for multimodal large language models,

Reference 29

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This paper cites Visionllm: Large language model is also an open-ended decoder for vision-centric tasks,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Visionllm: Large language model is also an open-ended decoder for vision-centric tasks,

Reference 30

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This paper cites Seeing what is not there: Learning context to determine where objects are missing,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Seeing what is not there: Learning context to determine where objects are missing,

Reference 31

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This paper cites Deep learning for environmental conservation,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Deep learning for environmental conservation,

Reference 32

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Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data YOLOv10: Real-Time End-to-End Object Detection

Reference 33

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This paper cites microsoft/Phi-3.5-vision-instruct,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data microsoft/Phi-3.5-vision-instruct,

Reference 34

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This paper cites Attention is all you need,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Attention is all you need,

Reference 35

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This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Retrieval-augmented generation for knowledge-intensive nlp tasks,

Reference 36

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This paper cites Guidelines for the application of IUCN Red List of Ecosystems Categories and Criteria: version 2.0,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Guidelines for the application of IUCN Red List of Ecosystems Categories and Criteria: version 2.0,

Reference 37

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Observation 1a899f03-6977-4e3b-968c-a580f33e97c6 · outbound

This paper cites The LEDA Traitbase: a database of life -history traits of the Northwest European flora,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data The LEDA Traitbase: a database of life -history traits of the Northwest European flora,

Reference 38

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Observation 02aeebf4-cfeb-47d7-9b42-23eb5a804242 · outbound

This paper cites Open Science principles for accelerating trait -based science across the Tree of Life,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Open Science principles for accelerating trait -based science across the Tree of Life,

Reference 39

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Observation a2f6bf9d-22e0-4b4f-a744-9b4269b0e8c7 · outbound

This paper cites Biocredits,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Biocredits,

Reference 40

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This paper cites Vision-language models for vision tasks: A survey,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Vision-language models for vision tasks: A survey,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:29:17.602426Z

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Observation 4dac524f-f9df-4273-8c06-da8949943a10 · outbound

This paper cites Real-time alerts from AI-enabled camera traps using the Iridium satellite network: A case-study in Gabon, Central Africa,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Real-time alerts from AI-enabled camera traps using the Iridium satellite network: A case-study in Gabon, Central Africa,

Reference 42

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Observation e4774155-ff8c-43e5-9db7-69cfc2c03083 · outbound

This paper cites An evaluation of platforms for processing camera-trap data using artificial intelligence,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data An evaluation of platforms for processing camera-trap data using artificial intelligence,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:29:17.583560Z

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Observation 455eab2f-5c95-465f-8b0d-e32a4d7e3092 · outbound

This paper cites Fine-tuning llama for multi-stage text retrieval,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Fine-tuning llama for multi-stage text retrieval,

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This paper cites The Faiss library.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data The Faiss library

Reference 45

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This paper cites A survey on performance metrics for object -detection algorithms,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data A survey on performance metrics for object -detection algorithms,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:29:17.570511Z

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Observation ecf79a7d-2038-4020-b1c1-bd7c4598f954 · outbound

This paper cites Faster R -CNN: Towards real -time object detection with region proposal networks,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Faster R -CNN: Towards real -time object detection with region proposal networks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:29:17.562414Z

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Observation 6ea400c0-2a2b-4099-a005-686d8eefafb7 · outbound

This paper cites Deep learning,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Deep learning,

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Observation c3de36a6-f465-404a-8b2e-5f1ba1288c87 · outbound

This paper cites Microsoft coco: Common objects in context,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Microsoft coco: Common objects in context,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:29:17.549071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 77dff0e4-d1c3-46e7-92ee-f12bc32277b2 · outbound

This paper cites CSPNet: A new backbone that can enhance learning capability of CNN,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data CSPNet: A new backbone that can enhance learning capability of CNN,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:29:17.541444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 69ddbc1c-3545-40b7-83ea-c16d28f60dbf · outbound

This paper cites Path aggregation network for instance segmentation,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Path aggregation network for instance segmentation,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:29:17.533582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 62506e8a-2174-49b0-870e-07a66f825ff0 · outbound

This paper cites Learning non -maximum suppression,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Learning non -maximum suppression,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:29:17.525576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bb8891c3-888e-4c63-8814-b5da8e84bbd1 · outbound

This paper cites Comprehensive Performance Evaluation of YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Comprehensive Performance Evaluation of YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T15:29:17.302705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2c40b85-8500-4139-b778-28b06a5658ff · outbound

This paper cites YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T15:29:17.305496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bd564e4-049b-47e5-8cd6-e219b397c914 · outbound

This paper cites Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:29:17.517089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 702aa9c0-304a-4006-89df-607aef4c08c9 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T15:29:17.310573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 727d5701-22b9-4e91-9504-a9dd66fc4beb · outbound

This paper cites Selective kernel networks,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Selective kernel networks,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T15:29:17.314280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a83c893-e2ce-42b0-a68d-9be21d5ae908 · outbound

This paper cites YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T15:29:17.317511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4840f6fb-6803-499e-9d57-c95f8d4c9e89 · outbound

This paper cites Creating large language model applications utilizing langchain: A primer on developing llm apps fast,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Creating large language model applications utilizing langchain: A primer on developing llm apps fast,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:29:17.503139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 645ea9d3-7aee-4d05-b1a4-477a66c6b00f · outbound

This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T15:29:17.323834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6add79c8-7be3-41c1-be4b-04b120b14232 · outbound

This paper cites Foundations of JSON schema,.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Foundations of JSON schema,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:29:17.493520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0ca8e3ae-cec8-4248-bedc-072b43c5fa62 · outbound

This paper cites Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T15:29:17.330034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers