Identification of climate change induced heat stress sensitive environments and prediction for diverse representative concentration pathways – A novel approach for tracking hotspots
Bibliographic record
Abstract
Abstract Climate change is unequivocal across economies and India owing to its distinct geography has been exposed to several climatic risks, especially in agriculture. Heat stress is a serious environmental problem posed by climate change, wherein mean temperature is expected to increase relatively more during wheat growing season affecting production and food security. In the milieu , the investigation is pioneered to predict heat stress sensitive wheat growing environments in India for research prioritization using a long term (30 years) historical daily data. The study has developed a methodological approach by integrating statistical downscaling of climate information and principal component analysis for computing heat stress intensity index (HSII) for 17 experiment locations across wheat growing environments. HSII were estimated for existing locations post testing for Levene’s homogeneity of variance, followed by prediction for three periods’ viz., early-future (2026-2050), mid-future (2051-2075) and far-future (2076-2100) under two emission scenarios namely RCP4.5 and RCP8.5. The results alarmed a radical shift in HSII of experiment locations from one period to another in both scenarios. Experiment locations with high index values for the existing environment has moved almost to lower category in the early future and subsequently shifted to higher position in the mid-future and far-future. The investigation also found that under projected RCP4.5, trial locations in peninsular zone need more emphasis, whereas in RCP8.5, peninsular zone coupled with central zone and north eastern plains zone have to be focused. Overall, the study develops a pragmatic approach in location prioritization across predicted periods which can be replicated to other regions. On policy front, rational allocation of research funds has been suggested to carry out field trials on climate change induced heat stress sensitive environments for sustaining the national wheat production apart from developing micro-level adaptation strategies to counter adverse effects of climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".