Spatial distribution of rorqual whales in the Strait of Jacques Cartier, Gulf of St. Lawrence, Quebec, Canada
Bibliographic record
Abstract
The spatial distribution of four species of rorqual whales was studied along the north shore of the Gulf of St. Lawrence during the months of June to October from 1989 to 2000. A research effort of 6511 hours at sea yielded 849 sightings of blue whales (Balaenoptera musculus), 5291 of finback whales (Balaenoptera physalus), 3822 of humpback whales (Megaptera novaeangliae) and 6489 of minke whales (Balaenoptera acutorostrata). Spatial and behavioural data were collected at sea using inflatable boats, and Global Positioning Systems (GPS) were used to obtain accurate positions. These data were plotted and analysed using a Geographic Information System (GIS) to test the hypotheses that patterns of distribution were not random, were associated with bathymetry and reflected specific differences in habitat use. The resulting maps illustrated the clustered distribution of rorqual whales linked to sea-bottom topography, probably associated with areas of local upwelling and increased productivity. Blue and fin whales shared almost the same distribution, humpback whales were found in slightly deeper, offshore waters whereas minke whales were more abundant in shallower waters. Little attention has been given until now to local patterns of distribution in the area and such information can be useful for practical management considerations. The results emphasise the importance of scale in ecological studies of marine mammals and the need for further research using additional oceanographic parameters, in order to better understand habitat selection.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".