Towards generalized benthic species recognition and quantification using computer vision
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it