Trends in phytoplankton communities within large marine ecosystems diverge from the global ocean
Bibliographic record
Abstract
Large marine ecosystems (LMEs) are highly productive regions of the world ocean under anthropogenic pressures; we analyzed trends in sea surface temperature (SST), cloud fraction (CF), and chlorophyll concentration (CHL) over the period 1998–2019. Trends in these parameters within LMEs diverged from the world ocean. SST and CF inside LMEs increased at greater rates inside LMEs, whereas CHL decreased at a greater rates. CHL declined in 86% of all LMEs and of those trends, 70% were statistically significant. Complementary analyses suggest phytoplankton functional types within LMEs have also diverged from those characteristic of the world ocean, most notably, the contribution of diatoms and dinoflagellates, which have declined within LMEs. LMEs appear to be warming rapidly and receiving less solar radiation than the world ocean, which may be contributing to changes at the base of the food chain. Despite increased fishing effort, fishery yields in LMEs have not increased, pointing to limitations related to productivity. These changes raise concerns over the stability of these ecosystems and their continued ability to support services to human populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".