Biophysical influences and Appendicularia abundance differentiate zooplankton and age-0 fish communities spanning subarctic and Arctic bioregions
Bibliographic record
Abstract
In the Canadian Arctic and subarctic, zooplankton and age-0 fish are highly sensitive to environmental variation, but boom-and-bust recruitment makes their response to environmental changes difficult to predict. We combined hydroacoustic and continuous plankton recorder (CPR) surveys to assess the main biophysical drivers influencing the distribution of zooplankton and age-0 fish in the epipelagic waters across a vast latitudinal gradient ranging from the Gulf of St. Lawrence (49°N) to northern Baffin Bay (77°N). Multivariate analyses suggested 2 distinct near-surface (~7 m) zooplankton communities, one in the subarctic combined Labrador Shelf/Gulf of St. Lawrence region and another in the Arctic Baffin Bay region, with differences primarily explained by latitude, surface chlorophyll concentration, and the abundance of Appendicularia. Surface currents connect the surface zooplankton communities on the Labrador Shelf with those in the Gulf of St. Lawrence, and the communities in the relatively productive waters of northern Baffin Bay with those in western Baffin Bay. Epipelagic (0-100 m) zooplankton abundance was most influenced by surface chlorophyll concentration, salinity, and latitude. Meanwhile zooplankton abundance, time of day, sea surface temperature, and latitude all had significant effects on epipelagic age-0 fish abundance, including polar cod Boreogadus saida . We conclude that food availability and latitudinal gradients in environmental conditions drive epipelagic community abundance and distribution in Arctic and subarctic regions. Furthermore, we demonstrate that combining CPR and hydroacoustics can improve the taxonomic and the vertical and latitudinal resolutions of the distributions of zooplankton and age-0 fish communities when opportunistic sampling is conducted during transit.
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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 | 0.000 | 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".