An analysis of geopotential fields in the Northern and Southern hemispheres under different natural climatic conditions during January
Bibliographic record
Abstract
Introduction. Atmospheric pressure is a crucial characteristic of the atmosphere. Changes in atmospheric pressure lead to changes in the entire set of weather conditions. The increase in global temperature affects all atmospheric characteristics, including atmospheric pressure. This paper examines the changes in atmospheric pressure near the Earth’s surface during the stabilization period and the second wave of global warming. Theoretical analysis. Based on the data on the height of the isobaric surface H0 (AT 1000 hPa), the average long-term fields of atmospheric pressure distribution on the globe during the stabilization period and during the second wave of global warming were constructed. The NCER/NCAR reanalysis data were used as the initial data. To assess the change in the fields, the difference in geopotential heights H0 in hPa was calculated by subtracting the average long-term field during the second wave of global warming from the average long-term field during the stabilization period. Conclusion. There are areas where the sea-level pressure in January was higher than during the stabilization period in the second wave. These areas are located in the eastern hemisphere, between the 120th and 150th meridians. The pressure decreased in the area of the Icelandic Low, the Canadian Archipelago, and along the coast of Antarctica, which is washed by the Indian Ocean.
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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 | 0.000 |
| 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 | 0.000 |
| 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".