Projecting annual air temperature changes to 2025 and beyond: implications for vegetable production worldwide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SUMMARY Data sets were accumulated of annual average maximum, minimum and mean air temperature from a range of sites worldwide, specifically from non-urban locations such as agricultural research institutes, universities and other rural or island locations for the period 1975–2011 or longer where data were available. The data sets were then analysed using linear regression to determine the rate and direction of change in temperature over the reference periods. This analysis was performed to provide vegetable scientists with likely future temperature change scenarios up to 2025 and 2050 (on the assumption that recent trends are maintained) so that breeding, agronomic and other related research programmes may better respond to potential challenges from abiotic and biotic stresses to vegetable production. Substantial variation was evident between sites and between time runs at specific sites. At some locations rapid increases in air temperature are projected, such as for sites in East Asia, but at other locations little change is evident; in rare cases, local cooling is shown. The implications of variability and change in air temperature in the context of constraints to vegetable production and the opportunities to exploit the range of genetic diversity available in climatically uncertain environments are discussed. It is believed that modern agricultural science can address successfully the problems raised by climate uncertainty, yet the lack of sufficient, immediate investment in horticultural disciplines worldwide places the world at severe risk of failing to attain effective food and nutritional security.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it