Integrating benthic surveys and community analysis through image management: a case study from a biodiversity special area in the Lower St. Lawrence Estuary, Québec, Canada
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The Manicouagan peninsula in the lower St. Lawrence Estuary is under consideration as a marine conservation zone, notable for its rich productivity and species diversity. To assist with the evaluation of habitats, consumer‐level cameras and image management software were used to make data from different surveys readily available for ecological analyses. Surveys of habitats from sand shoals to the deep‐water channel were conducted at stations at depths from 5 to 320 m using both physical sampling and a towed camera sled. An image catalogue database was used to compile field photos of grab and dredge samples (infauna) with laboratory images (conserved specimens) and the underwater tow photos (epifauna and demersal species). Software tools were used to leverage photo metadata and embed record information, including date, station, coordinates, and taxonomic names into the catalogue. Browsing images and filtering for metadata proved to be an efficient means for validating identification and location data. Catalogue records were exported for archiving to a network geodatabase, and subsequently used in analyses of community composition. Multivariate analyses performed with data from underwater photos and benthic grabs suggested similar zones of communities, even as the groups of species used in the classifications differed between the two methods. Underwater photo surveys in conjunction with image cataloguing tools are suggested as a low‐cost complement to physical sampling for the investigation of benthic habitats and their communities while greatly expanding the total area sampled.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".