Spatial distribution and seasonal occurrence of minke, humpback, fin and blue whales in the St. Lawrence Estuary
Bibliographic record
Abstract
The St. Lawrence Estuary (SLE) and the Gulf (GSL) are the feeding ground of several North Atlantic whales, including the endangered blue whale and fin whale (special concern), as well as minke and humpback whales. Vessel strikes are an important source of mortality for these species. To determine the spatial and temporal distribution of baleen whales in the SLE, analyses were conducted using four datasets: DFO’s aerial and boat surveys (1995-2017), Parks Canada and collaborators data from boat surveys (2006-2011), and observations obtained from whale watching activities monitoring (1994-2018) and a citizen science program (2008- 2018). Spatial modelling was used to identify important areas for the four species. The head of the Laurentian channel (HLC) appeared as a core area for minke, humpback and fin whales. Fin, humpback and blue whales were predicted to occur and were observed along the steep slopes of the Laurentian Channel (LC) (comprised in between 100-200 m isobaths). Blue whales were also found in waters deeper than 200 meters in the LC. Shallow water slopes (between 20-100 m isobaths) were identified as important habitat for minke whales. The data available did not allow for a full analysis of seasonal changes in baleen whale habitat use within the SLE. Maps of modelled habitat are representative of the spatial distribution of baleen whales within the SLE from May to October, the period which includes most sightings. Based on the combined datasets, minke whales were observed in the SLE from April to November; humpback and fin whales from May up to October and November, respectively; and blue whales from March to November. Spatial and temporal patterns of whale distribution in the SLE represent the integration of systematic surveys and other sources of data collected over the last 25 years; environmental variability may result in changes in whale distribution. Regular monitoring of the distribution of these species will be needed to ensure that management plans are achieving their objectives.
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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 | 0.000 | 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".