Linking phytoplankton phenology to salmon productivity along a north–south gradient in the Northeast Pacific Ocean
Bibliographic record
Abstract
We investigated spatial and temporal components of phytoplankton dynamics in the Northeast Pacific Ocean to better understand the mechanisms linking biological oceanographic conditions to productivity of 27 pink salmon (Oncorhynchus gorbuscha) stocks. Specifically, we used spatial covariance functions in combination with multistock spawner–recruit analyses to model relationships among satellite-derived chlorophyll a concentrations, initiation date of the spring phytoplankton bloom, and salmon productivity. For all variables, positive spatial covariation was strongest at the regional scale (0–800 km) with no covariation beyond 1500 km. Spring bloom timing was significantly correlated with salmon productivity for both northern (Alaska) and southern (British Columbia) populations, although the correlations were opposite in sign. An early spring bloom was associated with higher productivity for northern populations and lower productivity for southern populations. Furthermore, the spring bloom initiation date was always a better predictor of salmon productivity than mean chlorophyll a concentration. Our results suggest that changes in spring bloom timing resulting from natural climate variability or anthropogenic climate change could potentially cause latitudinal shifts in salmon productivity.
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".