Using BIIGLE as a collaborative identification tool for fisheries captures: an example from the benthic epifauna trawl surveys of the Canadian Beaufort Sea Marine Ecosystem Assessment (2021 to 2024)
Bibliographic record
Abstract
This report details the improvement of at-sea identification of epibenthic invertebrates collected during annual surveys of the Canadian Beaufort Sea Marine Ecosystem Assessment (CBS-MEA) through a collaborative and remote-based interregional workflow. The collaboration between DFO-Arctic and DFO-Quebec regional teams started in 2021 and, during that first year, a high rate (67 %) of at-sea identification errors was documented in the dataset. This highlighted the need to improve the approach for validating taxonomic identifications while optimizing knowledge sharing. During each survey, photos of whole trawl catches as well as each taxon collected were taken. Starting in 2022, the collaborative image annotation software BIIGLE was used in post-field work to share photos of specimens with digital labels (annotations) representing a scientific name. A taxonomic expert (DFO-Quebec) reviewed the epifauna biodiversity dataset and, if necessary, annotated the corrections directly through the photos in BIIGLE. The field team (DFO-Arctic) was then able to visually review taxonomic corrections and naming of unidentified taxa in the photos, enabling a better learning opportunity than reviewing revised taxonomic names in a spreadsheet dataset. From 2021 to 2024, the identification correction rate dropped from 67 % to 16 %. This underscores that an iterative collaboration, facilitated by usage of BIIGLE, greatly improved at-sea identification. This approach holds potential for broader application across other collaborative programs and projects related to fisheries captures within or outside DFO, demonstrating the value of taxonomic validation and use of digital tools in remote collaboration.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".