Impact of Climate Change and Variability on Wheat and Corn Production in Buenos Aires, Argentina
Bibliographic record
Abstract
From the Global Historical Climate Network (GHCN-V3), monthly mean summer (DJF) temperature (1856-2012) and total precipitation (1861-2012) are analyzed in correlation with four climate modes and sunspot number to better understand the role of teleconnections on Buenos Aires’ (Argentina) climate. A general increase in temperature and precipitation was observed. Temperature has increased by about 1.8°C and precipitation has increased by about 300 mm in the past century and a half. Indices of Arctic Oscillation (AO), Pacific North American (PNA), Antarctic Oscillation (AAO), and El Nino-Southern Oscillation (ENSO) are evaluated to study their effects on wheat and corn production and export. AO and PNA show strong relationships with precipitation and temperature received. AAO and ENSO show strong negative correlations with precipitation patterns and weak correlations with temperature. Sunspot Number shows a positive correlation with temperature. ENSO phases are strongly linked with the wheat and corn production and export; during El Nino Buenos Aires tends to experience extremely wet summer weather, causing soggy fields and extremely dry summer weather during La Nina causing drought. Both of these conditions result in reducing wheat and corn production and export.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".