Ekman Pumping and Mixed Layer Depth Variability over the Indo-Pacific Oceans during the El Nino and IOD Events
Bibliographic record
Abstract
The following study addresses the variability of Mixed Layer Depth (MLD) and Ekman pumping (W EK ) during the extreme El Nino and Indian Ocean Dipole (IOD) over the Indo-Pacific regions. Monthly anomalies considering the climatology of the period of 1980 through 2011 show that during the El Nino years, Ekman suction (positive Ekman pumping) is replaced with Ekman pumping (negative Ekman pumping) in the tropical eastern Pacific Ocean (88˚W-90˚W and 13˚S-15˚S) resulting in positive MLD anomalies, and the strong Ekman pumping may be the source for the deepened thermocline during El Nino. In the La Nina events shallow MLD exits in the tropical eastern Pacific Ocean, due to positive Ekman pumping. During the positive IOD (PIOD) events in the south eastern Indian Ocean (97˚E-100˚E and 2˚S-5˚S) MLD becomes shallow and positive Ekman pumping anomalies occur. During the negative IOD (NIOD) years opposite signs take place. Composite events of El Nino are compared with those of IOD, showing more Ekman pumping anomalies during IOD events as against less deviation in SST.
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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 | 0.000 | 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".