Long‐term variability of surface nutrient concentrations in the North Pacific
Bibliographic record
Abstract
Abstract We present the spatial distributions and temporal changes of the long‐term variability of surface nutrient concentrations in the North Pacific by using nutrient samples collected by volunteer ships and research vessels from 1961 to 2012. Nutrient samples are optimally interpolated onto 1° × 1° monthly grid boxes. When the Pacific Decadal Oscillation is in its positive phase, nutrient concentrations in the western North Pacific are significantly higher than the climatological means, and those in the eastern North Pacific are significantly lower. When the North Pacific Gyre Oscillation is in its positive phase, nutrient concentrations in the subarctic are significantly higher than the climatological means. The trends of phosphate and silicate averaged over the North Pacific are −0.012 ± 0.005 µmol l−1 decade−1 and −0.38 ± 0.13 µmol l−1 decade−1, whereas the nitrate trend is not significant (0.01 ± 0.13 µmol l−1 decade−1).
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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 | 0.001 |
| 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) | 0.000 | 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".