Clustering to Characterize Extreme Marine Conditions for the Benthic Region of the Northeastern Pacific Continental Margin
Bibliographic record
Abstract
Abstract Anthropogenic emissions lead to ocean warming, deoxygenation and acidification. Superimposed on long‐term trends are episodic extremes of temperature, oxygen, and acidity. Here we present an innovative method for assessing single and compound 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, 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. Compound extremes (most commonly of and acidification) are rare but show an increasing trend in some clusters. Potential predictability of occurrence of extremes is demonstrated by correlation with basin‐scale climate variability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".