Bibliographic record
Abstract
Abstract In the last decade, Fred Sanders was often critical of current surface analysis techniques. This led to his promoting the use of surface potential temperatures to distinguish between fronts, baroclinic troughs, and non-frontal baroclinic zones, and to the development of a climatology of surface baroclinic zones. In this paper, criticisms of current surface analysis techniques and the usefulness of surface potential temperature analyses are discussed. Case examples are used to compare potential temperature analyses and current National Centers for Environmental Prediction analyses. The 1-yr climatology of Sanders and Hoffman is reconstructed using a composite technique. Annual and seasonal mean potential temperature analyses over the continental United States, southern Canada, northern Mexico, and adjacent coastal waters are presented. In addition, gridpoint frequencies of moderate and strong potential temperature gradients are calculated. The results of the mean potential temperature analyses show that moderate and strong surface baroclinic zones are favored along the coastlines and the slopes of the North American cordillera. Additional subsynoptic details, not found in Sanders and Hoffman, are identified. The availability of the composite results allows for the calculation of potential temperature gradient anomalies. It is shown that these anomalies can be used to identify significant frontal baroclinic zones that are associated with weak potential temperature gradients. Together the results and reviews in this paper show that surface potential temperature analyses are a valuable forecasting and analysis tool allowing analysts to distinguish and identify fronts, baroclinic troughs, and nonfrontal baroclinic zones.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".