Conservation implications of white shark (<i>Carcharodon carcharias</i>) behaviour at the northern extent of their range in the Northwest Atlantic
Bibliographic record
Abstract
Assessing progress for the endangered white shark ( Carcharodon carcharias) relative to Canadian conservation objectives requires understanding distribution patterns. From the largest tagging data set in the Northwest Atlantic (2010–2020; 272 deployments), we determined the proportion of the population detected in Canadian waters, characterized patterns in occupancy, and explored the behavioural characteristics of animals while in Canadian waters versus elsewhere in their range. The component of the population detected in Canadian waters annually was highly variable, yet proportionately small. Juveniles and subadults were 4.7 and 3.4 times more likely, respectively, to move northward than adults. From June to November, all pop-up satellite archival tagged white sharks remained primarily in coastal locations within the 200 m bathymetric contour and exhibited shallow diving behaviour within the top 100 m of the water column. However, individuals in Canadian waters experienced a more restricted temperature range and used proportionately less of the water column. Accounting for behavioural effects on distribution when predicting habitat use from environmental associations will become critical to evaluate the population-level impact of recovery actions implemented under Canadian legislation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".