Towards identifying areas at climatological risk of desertification using the Köppen–Geiger classification and FAO aridity index
Bibliographic record
Abstract
We coupled the information obtained from the Köppen-Geiger (KG) climate classification and the FAO Aridity Index (AI) to provide an overview of the most evident global changes in climate regimes from 1951-80 to 1981-2010. Based on a set of sixteen auxiliary variables and special conditions derived from mean temperature (TM) and precipitation (RR) values, KG classifies climate into five major classes (arid, tropical, temperate, continental, polar), that are further sub-categorized for a total of thirty classes. AI is based on the ratio between the annual total RR and potential evapo-transpiration (PET) and classifies climate into eight classes, from desert to humid. To compute the indicators, we used a combination of two datasets on a 0.5˚ x 0.5˚ global grid: RR from Full Data Reanalysis (version 6.0) provided by the Global Precipitation Climatology Centre (GPCC), TM and PET provided by the Climate Research Unit of the University of East Anglia (CRUTS version 3.20). Both KG and AI agree: from 1951-80 to 1981-2010 the cold areas decreased, whilst the arid areas globally increased except of the Americas. Some hot spots at high desertification risk have been detected: North-Eastern Brazil, Southern Sahel, Zambia and Botswana, Southern Spain, North Eastern China, Central India, and Southern Argentina. We also discuss the change from continental to temperate climate in Central Europe, the shift from tundra to continental climate in Alaska, Canada and North-Eastern Russia, and the widening of the tropical belt.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".