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Paper Citation Record · LEDGER

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling

As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2603.10174.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2603.10174 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T23:50:28.153875Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T15:05:26.548744Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-10T15:05:32.416728Z

Reference resolution

31 of 31 outbound references displayed

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Outbound references

Observation 5ffd5a66-9bf4-4eb5-9a26-97bf56ae66a0 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling DINOv2: Learning Robust Visual Features without Supervision

Reference 1

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Observation 3394da1e-da3c-4428-8ce7-050a913a9d88 · outbound

This paper cites Segment anything,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Segment anything,

Reference 2

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Observation 35eff88e-fe08-4494-80c9-09e2f557d74d · outbound

This paper cites The coralscapes dataset: Semantic scene understanding in coral reefs,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling The coralscapes dataset: Semantic scene understanding in coral reefs,

Reference 3

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Observation 21a0a6b1-47e4-43dd-9422-a88d9e30cf11 · outbound

This paper cites Coralscop: Segment any coral image on this planet,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Coralscop: Segment any coral image on this planet,

Reference 4

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Observation d20af945-0c9d-488d-b542-2b33c4705095 · outbound

This paper cites Agisoft Metashape Professional (version 2.2.0),.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Agisoft Metashape Professional (version 2.2.0),

Reference 5

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Observation 314428b4-f39b-4f8e-bad0-09d659834fd3 · outbound

This paper cites The hugin 1000 autonomous underwater vehicle for military applications,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling The hugin 1000 autonomous underwater vehicle for military applications,

Reference 6

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Observation 466478e3-8e0c-4c87-ad50-101a2207b716 · outbound

This paper cites Exploring the aurora vent field: 4000 m under ice with the nui hybrid remotely operated vehicle,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Exploring the aurora vent field: 4000 m under ice with the nui hybrid remotely operated vehicle,

Reference 7

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Observation b34791f0-7ff7-407b-a887-b7bec9d366f7 · outbound

This paper cites Coverage of known spaces: The boustrophedon cellular decomposition,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Coverage of known spaces: The boustrophedon cellular decomposition,

Reference 8

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Observation 1a369a45-b543-4539-9f30-6ae47d57cf40 · outbound

This paper cites Automated annotation of coral reef survey images,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Automated annotation of coral reef survey images,

Reference 9

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Observation 379bab1b-64c4-4d8a-8714-af4c1acc4901 · outbound

This paper cites Deep learning for coral classification,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Deep learning for coral classification,

Reference 10

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Observation dbd23c31-b472-4447-8f69-5a6206aebd0b · outbound

This paper cites Monitoring of coral reefs using artificial intelligence: A feasible and cost-effective ap- proach,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Monitoring of coral reefs using artificial intelligence: A feasible and cost-effective ap- proach,

Reference 11

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Observation a5532ea1-f1a6-46d4-a022-5ca61ed48f54 · outbound

This paper cites A new deep learning engine for coralnet,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling A new deep learning engine for coralnet,

Reference 12

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Observation c683bba9-ac0f-4dd0-b775-324fb2c1c672 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Learning transferable visual models from natural language supervision,

Reference 13

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Observation f1d806f5-8780-4e82-b807-b39150e5607f · outbound

This paper cites Emerging properties in self-supervised vision trans- formers,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Emerging properties in self-supervised vision trans- formers,

Reference 14

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Observation ba02e0d7-7cd9-4f8f-aa13-cd7a8324982d · outbound

This paper cites Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation

Reference 15

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Observation e772a24d-ec4d-4ec9-ad39-298326d703c3 · outbound

This paper cites Deep ViT Features as Dense Visual Descriptors.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Deep ViT Features as Dense Visual Descriptors

Reference 16

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Observation 3709dac6-8ea4-4549-bdfb-86de09dacbe8 · outbound

This paper cites Dinov2 based self supervised learning for few shot medical image segmentation,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Dinov2 based self supervised learning for few shot medical image segmentation,

Reference 17

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Observation cc16484b-b73b-4d26-ae03-b1143501af14 · outbound

This paper cites An evaluation of a pre-trained transformer-based self-distillation model (dinov2) for cross-domain plant species identification,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling An evaluation of a pre-trained transformer-based self-distillation model (dinov2) for cross-domain plant species identification,

Reference 18

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Observation 79960790-b8aa-4399-9f91-7d4c7d847cc7 · outbound

This paper cites Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning,

Reference 19

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Observation f3e78f1e-af0e-40bf-82f3-2b7be4877e8d · outbound

This paper cites Unsupervised Semantic Segmentation by Distilling Feature Correspondences.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Unsupervised Semantic Segmentation by Distilling Feature Correspondences

Reference 20

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Observation 6ba2418d-decd-46a0-a422-1df66dc53e3e · outbound

This paper cites Human-in-the-loop segmentation of multi-species coral imagery,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Human-in-the-loop segmentation of multi-species coral imagery,

Reference 21

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Observation f04c25cd-6e2e-4605-adb8-2b23fc944890 · outbound

This paper cites Information-guided robotic maximum seek-and-sample in partially observable continuous environments,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Information-guided robotic maximum seek-and-sample in partially observable continuous environments,

Reference 22

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Observation 103ab60a-2b2b-4647-83b6-ac81337e269b · outbound

This paper cites Informative path planning for an autonomous underwater vehicle,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Informative path planning for an autonomous underwater vehicle,

Reference 23

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Observation e1f314f1-d252-4796-ad2b-9e0bc0572309 · outbound

This paper cites Large area 3d reconstructions from underwater surveys,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Large area 3d reconstructions from underwater surveys,

Reference 24

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Observation 57f670e7-1d55-4a2d-82bc-1c936d125cb1 · outbound

This paper cites Efficient 3d real-time adaptive auv sampling of a river plume front,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Efficient 3d real-time adaptive auv sampling of a river plume front,

Reference 25

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Observation 524295c0-1a17-47e7-9817-28cfb016a938 · outbound

This paper cites Autonomous adaptive ex- ploration using realtime online spatiotemporal topic modeling,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Autonomous adaptive ex- ploration using realtime online spatiotemporal topic modeling,

Reference 26

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Observation 9494dbb3-221e-4adb-ba7d-d1bf47dfe4ea · outbound

This paper cites Human diver-inspired visual navigation: Towards coverage path planning of shipwrecks,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Human diver-inspired visual navigation: Towards coverage path planning of shipwrecks,

Reference 27

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Observation 976225d3-90e2-45cd-a34f-6c6fcde64c8f · outbound

This paper cites Uivnav: Underwater information-driven vision- based navigation via imitation learning,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Uivnav: Underwater information-driven vision- based navigation via imitation learning,

Reference 28

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Observation aae7a820-f291-4fb3-8e29-4faaa4e13d23 · outbound

This paper cites Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles

Reference 29

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Observation 0b04f2ac-1680-4953-ab8b-820511aa9ea1 · outbound

This paper cites Adaptive multi-altitude search and sampling of sparsely distributed natural phenomena,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Adaptive multi-altitude search and sampling of sparsely distributed natural phenomena,

Reference 30

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Observation acef549e-ffcc-4eca-8b61-7bff9c64a9b1 · outbound

This paper cites Active reward learning for co- robotic vision based exploration in bandwidth limited environments,.

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling Active reward learning for co- robotic vision based exploration in bandwidth limited environments,

Reference 31

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Pith citing papers

Observation 184fdc95-0104-46db-b549-5af6f0f74fee · inbound

DINO-Explorer: Active Underwater Discovery via Ego-Motion Compensated Semantic Predictive Coding cites this paper.

DINO-Explorer: Active Underwater Discovery via Ego-Motion Compensated Semantic Predictive Coding Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling

Reference 15

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arxiv_id, observed 2026-07-07T03:18:13.088985Z

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