Towards generalized benthic species recognition and quantification using computer vision
Bibliographic record
Abstract
Seabed resource exploitation and conservation efforts are extending to offshore areas where the distribution of benthic epifauna (animals living on the seafloor) is unknown. There is a need to survey these areas to determine how biodiversity is distributed spatially and to evaluate and monitor ecosystem states. Seafloor imagery, collected by underwater vehicles, offer a means for large-scale characterization of benthic communities. A single submersible dive can image thousands of square metres of seabed using video and digital still cameras. As manual, human-based analysis lacks large-scale feasibility, there is a need to develop efficient and rapid techniques for automatically extracting biological information from this raw imagery. To meet this need, underwater computer vision algorithms are being developed for the automatic recognition and quantification of benthic organisms. Focusing on intelligent analysis of distinct local image features, the work has the potential to overcome the unique challenges associated with visually interpreting benthic communities. The current incarnation of the system is a significant step towards generalized benthic species mapping, and its feature-based nature offers several advantages over existing technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".