Bibliographic record
Abstract
The“climate draft”often occurs in the coupling process of the atmospheric general circulation model (AGCM) andoceanic general circulation model (OGCM).One of the main methods to overcome the“climate draft”is to simulate theflow and temperature fields in the low-layer correctly.Therefore we designed a three-level AGCM including a planeta-ry boundary layer (PBL) and have run it seven model years to do climate simulation.The results show that the simulatedlower level air flow,surface air temperature and sea-level pressure in January and July,approximate to the climate av-erage fields,especially in Asian monsoon area.The simulated upper level flow and geopotential height are also in betteragreement with the observed fields.Moreover,the two westerly jets over the northern and southern sides of theQinghai-Xizang Plateau in winter,the disappearance of its southern subtropical jet during the seasonal transition fromspring to summer,the establishment of the two easterly jets near the equator and over the subtropical region during theseasonal transition,are also simulated well.In the mainland of China,the seasonal abrupt shift of the rainfall belt,suchas the Meiyu belt in South China during April to May,which jumps to the Changjiang River region in June,again jumpsback to the north China in July,and rapidly withdraws to the south in August,are simulated very well.Now we arecoupling this model to a global six-level OGCM and nesting a fine mesh (1°×1.25°)regional climate model over Chinaarea with it.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.990 | 0.992 |
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".