Clustering to characterize extreme marine conditions for the benthic region of the Northeastern Pacific continental margin
Bibliographic record
Abstract
Anthropogenic CO2 emissions lead to ocean warming, deoxygenation and acidification. Superimposed on the long-term trends are episodic extremes of temperature, oxygen, and acidity. Here we present an innovative method for assessing single and multiple stressor extremes using a high-resolution regional model of the Northeastern Pacific Ocean. We use an unsupervised clustering approach to identify regions with similar habitat characteristics near the seafloor. We define extreme thresholds seasonally using a fixed baseline (1996-2020) within each cluster, and quantify the fraction of ocean waters that exceed these thresholds for both single and compound stressors. A substantial number of single stressor extremes occur, but compound extremes are rare; most compound extremes involve O2 and acidification. Large-scale climate indices (e.g., North Pacific Gyre Oscillation) are correlated with the fraction of extreme waters. Ocean upwelling and basin-scale climate variability have a strong influence on extreme conditions in this region.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".