Bibliographic record
Abstract
Large-scale modes of variations in sea surface temperatures (SSTs) and the tropospheric circulation are examined with major principal components utilizing-monthly mean fields of the global SSTs and Northern Hemisphere geo-potential height (Z) at 700, 500 and 300 mb levels. The data period covered is from 1955 to 1992. It is found that the heterogeneity of SST data due to availability of satellite observations and difference of analysis schemes may result in a large systematic bias in the dataset. However, the bias may be effectively corrected through elimination of an appropriate principal component. The first three components of monthly SST and Z fields during the 38-year period are presented. The El Nino-Southern Oscillation (ENSO) mode of variations is observed in both 1st and 2nd components of SSTs. The inter-annual viariations of principal components of monthly SST and Z fields are utilized to probe the association of SST components and Z components. Further, cross-correlation patterns of principal components of SSTs in reference to the tropospheric circulation are studied with 500 and 300 mb fields. It is hown that the tropospheric response to SST anomalies are consistent at 500 and 300 mb levels, and the seasonal-range predictability of the tropospheric circulation is recognized with SST anomaly fields.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.992 | 0.994 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".