Systematic assessment of the increasing presence of white sharks in Atlantic Canadian waters
Bibliographic record
Abstract
This study presents the first systematic assessment of trends in white shark Carcharodon carcharias presence in Atlantic Canadian waters (ACWs) using 2 standardized acoustic monitoring arrays—the Halifax and Cabot Strait lines—deployed by the Ocean Tracking Network between 2014 and 2023. We evaluated annual changes in the probability that acoustically tagged white sharks (n = 260) migrate from US waters to these arrays and the extent of their seasonal detection period in ACWs. Our analysis revealed an increase in the probability of white sharks migrating into ACWs, starting around 2019-2021. The probability of migrating to the Halifax line increased 2.4-fold [1.8, 3.3] (median [95% credible interval]), from 0.18 [0.13, 0.24] to 0.44 [0.38, 0.49], and to the Cabot Strait line increased 3.7-fold [2.4, 6.2], from 0.08 [0.05, 0.11] to 0.28 [0.24, 0.33], between 2018 (n = 63) and 2022 (n = 159). We also observed a 1.4-fold [1.2, 1.8] increase in the seasonal detection period in ACWs, extending from 48 d [40, 58] during 2014-2018 (n = 13) to 70 d [63, 78] in 2023 (n = 76). While our analysis robustly detected an increase in white shark presence in ACWs, limited data in the early years introduced uncertainty in quantifying the amplitude of the migration probability trend. The reported numbers should be interpreted carefully. The rapid timespan of this increase highlights the need to investigate causal mechanisms structuring white shark presence in ACWs, which is essential for the management of the Northwest Atlantic population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".