(www.interscience.wiley.com) DOI: 10.1002/joc.1423 Remote weather associated with North Pacific subtropical sea-level high properties
Bibliographic record
Abstract
Remote events influencing North Pacific (NP) subtropical high properties in monthly and daily data are identified. Variability in the NP during summer is far more strongly dominated by midlatitude events than in South Pacific (SP); low-pass filtering is required to see tropical associations. The dominant pattern in composites, correlations, and regressions is a midlatitude wave train. A stronger NP high was led by higher sea-level pressure (SLP) just east of Japan and lower SLP over central Canada and to a lesser extent over western tropical Pacific. Various mechanisms have been proposed to force the NP high: (1) Heating over southwestern North America (with cooling off the west coast). However, higher temperatures over North America follow stronger SLP over the NP high and occur much further east than postulated. Higher SLP occurs where temperatures are lower over western North America and adjacent ocean. Thermal pattern is consistent with temperature advection between NP high and Canadian low. (2) Precipitation over and near Central America. However, SLP increase on the SE side of the high is led by higher SLP (and higher outgoing longwave radiation (OLR)) along the west coast of Mexico and Central America. Normalized regressions find a very weak lower OLR in North American monsoon preceding stronger NP high, but the region is much smaller in size and magnitude than other significant areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.787 | 0.794 |
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".