Multidecadal trends in North American Atlantic salmon (<i>Salmo salar</i>) stocks and climate trends relevant to juvenile survival
Bibliographic record
Abstract
Landings of North American Atlantic salmon (Salmo salar) over the past century show multidecadal patterns, which most recently characterize unprecedented declines in abundance. Stock size is compared with sea surface temperature (SST) data in the marine nurseries of post-smolt Atlantic salmon. A previously described correlation between stock abundance and winter SST conditions was again documented; however, of more relevance to the survival of salmon post-smolts, a correlation was also observed between abundance and spring SST in the Gulf of St. Lawrence. The relevance of the winter SST correlation was further investigated by considering winter conditions in the freshwater nurseries as a factor causing elevated overwintering mortality of pre-migrant parr. The salmon abundance time series was compared with air temperature and rainfall trends averaged over time and space. Air temperature and rainfall do not appear to be significant environmental variables in shaping salmon recruitment. The timing of smolt runs appears to be out of synchronization with ocean conditions in the post-smolt nursery areas. The relationship between marine and freshwater impacts may change with changing climate conditions. Persistent positive phase forcing in the North Atlantic Oscillation raises the concern that recent declines in Atlantic salmon are, in part, due to global climate change.
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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 | 0.000 | 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".