Influences of extratropical water masses on equatorial Pacific cold tongue variability during the past 160 ka as revealed by faunal evidence of planktic foraminifers
Bibliographic record
Abstract
Abstract Glacial cooling (∼1–5°C) in the eastern equatorial Pacific (EEP) cold tongue is often attributed to increased equatorial upwelling, stronger advection from the Peru–Chile Current (PCC), and to the more remote subpolar southeastern Pacific water mass. However, evidence is scarce for identifying unambiguously which process plays a more important role in driving the large glacial cooling in the EEP. To address this question, here we adopt a faunal calibration approach using planktic foraminifers with a new compilation of coretop data from the eastern Pacific, and present new downcore variation data of fauna assemblage and estimated sea surface temperatures (SSTs) for the past 160 ka (Marine Isotope Stage (MIS) 6) from ODP Site 1240 in the EEP. With significant improvement achieved by adding more coretop data from the eastern boundary current, our downcore calibration results indicate that most of the glacial cooling episodes over the past 160 ka in the EEP are attributable to increased influence from the subpolar water mass from high latitudes of the southern Pacific. By applying this new calibration of the fauna SST transfer function to a latitudinal transect of eastern Pacific (EP) cores, we find that the subpolar water mass has been a major dynamic contributor to EEP cold tongue cooling since MIS 6. Copyright © 2012 John Wiley & Sons, Ltd.
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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".