North Atlantic right whale ( <i>Eubalaena glacialis</i> ) and its food: (I) a spatial climatology of <i>Calanus</i> biomass and potential foraging habitats in Canadian waters
Bibliographic record
Abstract
Abstract This study aimed at identifying potentially suitable foraging habitats for the North Atlantic right whale (NARW; Eubalaena glacialis) in the Gulf of St Lawrence (GSL), on the Scotian Shelf (SS) and in the Bay of Fundy (BoF), Canada, based on the distribution densities of their main prey, Calanus copepod species. More than 4800 historical Calanus spp. water column integrated samples as well as 221 vertically stratified sampling stations were used to create a 3D (latitude, longitude and vertical) climatology of Calanus spp. biomass densities for spring and summer–fall when NARW are feeding in Canadian waters. We then combined this 3D preyscape with bio-energetic considerations to highlight potentially suitable NARW foraging habitats in the region. Our 3D climatological approach successfully identified the known feeding areas of Grand Manan (BoF) and Roseway Basin (western SS), confirming its validity. Expanding our analyses to the GSL and other parts of the SS, we identified in both regions areas previously unknown where Calanus spp. biomass densities exceeded minimum levels suitable for foraging NARW. Our results represent a key contribution to the identification of important foraging areas for NARW in Canadian waters, especially in the context of climate change and the documented shift in NARW distribution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".