Evidence map›Paper›PMID 40546741›Full record

ArticleFrontiers in robotics and AI2025

Is AI currently capable of identifying wild oysters? A comparison of human annotators against the AI model, ODYSSEE.

Brendan Campbell, Alan Williams, Kleio Baxevani, Alyssa Campbell, Rushabh Dhoke, Rileigh E Hudock, Xiaomin Lin, Vivek Mange, Bernhard Neuberger, Arjun Suresh and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in robotics and AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Brendan CampbellUniversity of Delaware, College of Earth Ocean & Environment, School of Marine Science & Policy, Lewes, DE, United States.
Alan WilliamsUniversity of Maryland Center for Environmental Science, Horn Point Laboratory, Cambridge, MD, United States.
Kleio BaxevaniUniversity of Delaware, College of Earth Ocean & Environment, School of Marine Science & Policy, Lewes, DE, United States.
Alyssa CampbellUniversity of Delaware, College of Earth Ocean & Environment, School of Marine Science & Policy, Lewes, DE, United States.
Rushabh DhokeUniversity of Delaware, Center for Autonomous & Robotic Systems, Newark, DE, United States.
Rileigh E HudockUniversity of Delaware, College of Earth Ocean & Environment, School of Marine Science & Policy, Lewes, DE, United States.
Xiaomin LinUniversity of Maryland, Maryland Robotics Center, College Park, MD, United States.
Vivek MangeUniversity of Delaware, Center for Autonomous & Robotic Systems, Newark, DE, United States.
Bernhard NeubergerDepartment of Industrial Engineering, University of Applied Science Technikum, Wien, Austria.
Arjun SureshUniversity of Maryland, Maryland Robotics Center, College Park, MD, United States.
Alhim VeraUniversity of Cincinnati, Department of Aerospace Engineering & Engineering Mechanics, Cincinnati, OH, United States.
Arthur TrembanisUniversity of Delaware, College of Earth Ocean & Environment, School of Marine Science & Policy, Lewes, DE, United States.
Herbert G TannerUniversity of Delaware, Center for Autonomous & Robotic Systems, Newark, DE, United States.
Edward HaleUniversity of Delaware, College of Earth Ocean & Environment, School of Marine Science & Policy, Lewes, DE, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oysters are ecologically and commercially important species that require frequent monitoring to track population demographics (e.g., abundance, growth, mortality). Current methods of monitoring oyster reefs often require destructive sampling methods and extensive manual effort. However, these methods are destructive and are suboptimal for small-scale or sensitive environments. A recent alternative, the ODYSSEE model, was developed to use deep learning techniques to identify live oysters using video or images taken in the field of oyster reefs to assess abundance. The validity of this model in identifying live oysters on a reef was compared to expert and non-expert annotators. In addition, we identified potential sources of prediction error. Although the model can make inferences significantly faster than expert and non-expert annotators (39.6 s,

Indexed as

confusion matrixdeep learningimage identificationoyster aquaculturereef ecologyYOLOv10

Identifiers

PMID40546741
PMCPMC12178886

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.