Effect of seafloor depth on phytoplankton blooms in high‐nitrate, low‐chlorophyll (HNLC) regions
Bibliographic record
Abstract
We calculated correlations between seafloor depth and phytoplankton blooms in all three main high‐nitrate, low‐chlorophyll (HNLC) regions, but with a particular focus on the subarctic North Pacific area. It has long been known that the central parts of the east and west subarctic North Pacific are HNLC regions. The deep western basin of the Bering Sea is also HNLC, whereas the wide continental shelf of the eastern Bering Sea is not. We carried out a statistical comparison of spatial maps of (1) seafloor depth and (2) chlorophyll a concentration from satellite data. This comparison reveals that shallow waters have, on average, higher peak chlorophyll a concentrations (more intense phytoplankton blooms) than deep waters (p ≪ 0.01). Possible artifacts of the satellite data are considered but it is concluded that the signal is genuine, in part because the same patterns are found in in situ data. There are several possible explanations for the observed correlation; the most probable, we suggest, is that iron diffusing out of the seafloor causes alleviation of iron limitation in shallow waters.
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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 | 0.001 | 0.003 |
| 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".