Dry climates—past and present
Bibliographic record
Abstract
Dry climates and their mechanisms have been receiving considerable recent attention from scientists and government. Much of this interest relates to the problems of desertification and man’s possible role in inadvertent climate modification. This topic has been the subject of several recent syntheses (Sherbrooke and Paylore, 1973; Rapp, 1974; Glantz, 1976; Pay-lore and Haney, 1976; Hare, 1977) and is not treated directly here. Instead, this review examines recent climatological research into dry climates and their change through time. I Basic climatic characteristics Arid and semi-arid conditions affect approximately 3 per cent of the earth’s land surface. The extent of dry climates has been delimited by several writers. Meigs (1953) based his definitions on the moisture index (Im) of Thornthwaite relating available precipitation to potential evapo-transpiration from a moist surface. Budyko (1974) used the radiational index of dryness (D=Ro/LP, where Ro=mean annual net radiation for a
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".