Quantitative estimates of the movement and distribution of North Atlantic right whales along the northeast coast of North America
Bibliographic record
Abstract
General movement patterns for North Atlantic right whales are known, but quantitative season-specific estimates of individual movements and the resultant distributions do not exist. We use a Brownian Bridge movement model to estimate individual movement patterns and spatial probability distributions using time-and location-specific photo-identified right whales from 1978 through 2007 to produce monthly estimates of movement and distribution patterns for the population in the NW Atlantic, from Cape Cod northward. For comparative purposes we also estimate right whale transition probabilities among ocean regions to estimate rates of emigration and immigration, likely destinations, and monthly regionally specific population estimates. Areas were identified that right whales may frequent and that are potential locations of the regularly unaccounted proportion of the population. These areas, requiring additional survey effort, include the Gulf of St. Lawrence, Scotian Shelf, Columbia Ledges and western Jordan Basin. Our results show that along the northeast Atlantic coasts of Canada and the USA, right whales annually migrate in a general counter-clockwise pattern; north and east along the continental shelf in the spring and summer, and south and west along the coast during autumn and winter. The results also provide quantitative spatio-temporal estimates of right whales for all regions, including those that are rarely or never surveyed. The spatial probability distributions that we provide can be used in the future to quantitatively evaluate risks to right whales from human activities, particularly vessel traffic and commercial fishing, and thereby increase our ability to manage the risks and improve right whale conservation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".