Linking acoustics and finite‐time <scp>L</scp>yapunov exponents reveals areas and mechanisms of krill aggregation within the <scp>G</scp>ulf of <scp>S</scp>t. <scp>L</scp>awrence, eastern <scp>C</scp>anada
Bibliographic record
Abstract
Abstract The Gulf of St. Lawrence (GSL) is a feeding ground for several baleen whale species from the North Atlantic, providing them with an abundant supply of krill during their seasonal presence. Krill aggregations are found along the abrupt topography formed by the deep channels, but the dynamics of krill aggregations have not yet been characterized at the scale of the whole GSL. In this study, we combined extensive dual‐frequency acoustic observations of krill and Lagrangian numerical simulations to identify the recurrent areas of krill accumulation in summer and the mesoscale circulation mechanisms responsible for their formation. Throughout the GSL, the topographic forcing of the surface circulation appeared essential in forming convergence zones where observed krill concentrations were systematically higher than average, and within which most of the densest patches were observed. This approach can help in defining the dynamics of the feeding habitat of baleen whales in the GSL, in particular blue and fin whales whose diet is dominated by krill.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".