First estimates of Greenland shark (Somniosus microcephalus) local abundances in Arctic waters
Bibliographic record
Abstract
Baited remote underwater video cameras were deployed in the Eastern Canadian Arctic, for the purpose of estimating local densities of the long-lived Greenland shark within five deep-water, data-poor regions of interest for fisheries development and marine conservation in Nunavut, Canada. A total of 31 camera deployments occurred between July-September in 2015 and 2016 during joint exploratory fishing and scientific cruises. Greenland sharks appeared at 80% of deployments. A total of 142 individuals were identified and no individuals were observed in more than one deployment. Estimates of Greenland shark abundance and biomass were calculated from averaged times of first arrival, video-derived swimming speed and length data, and local current speed estimates. Density estimates varied 1-15 fold among regions; being highest in warmer (>0 °C), deeper areas and lowest in shallow, sub-zero temperature regions. These baited camera results illustrate the ubiquity of this elusive species and suggest that Nunavut's Lancaster Sound eco-zone may be of particular importance for Greenland shark, a potentially vulnerable Arctic species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".