Climate and juvenile recruitment as drivers of Arctic cod (<i>Boreogadus saida</i>) dynamics in two Canadian Arctic seas
Bibliographic record
Abstract
Arctic cod (Boreogadus saida) is the most abundant forage fish species in Arctic seas and plays a pivotal role in the transfer of energy between zooplankton and top predators. The dominance of Arctic cod and the Arctic’s relatively low biodiversity interact such that changing population dynamics of Arctic cod have cascading effects on whole Arctic marine ecosystems. Over the last decades, warming in the Arctic has led to a decline in Arctic cod populations in the Barents Sea, but in the Canadian Arctic these conditions have been correlated with up to a 10-fold higher biomass of age-0 Arctic cod at the end of summer. However, whether this enhanced larval survival with warmer waters endures through age-1+ populations is unknown. A better understanding of spatial variation in the response of Arctic cod populations to environmental conditions is critical to forecast future changes in Arctic ecosystems. Here, we rely on a 17-year time series of acoustic-trawl surveys (2003–2019) to test whether ice-breakup date, sea surface temperature, zooplankton density, and Arctic climate indices during early life stages affect the subsequent recruitment of age-1+ Arctic cod in the Beaufort Sea and Baffin Bay. In the Beaufort Sea, the biomass of age-1+ Arctic cod correlated with both Arctic Oscillation indices and age-0 biomass of the previous year. In Baffin Bay, the biomass of age-1+ Arctic cod correlated with previous-year North Atlantic Oscillation indices and the timing of ice breakup. This study demonstrates that climate and environmental conditions experienced during the early life stages drive the recruitment of the age-1+ Arctic cod population and helps to quantify spatial variation in the main environmental drivers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".