Occurrence of extreme temperature events – A Probable risk on natural rubber cultivation
Bibliographic record
Abstract
Climate change and occurrence of extreme temperature events were studied in Kottayam, a major rubber growing district in Kerala. Occurrence of extreme temperature events can affect the livelihood of rubber growers apart from the ecological impact. The present study was conducted by analysing the occurrence of extreme temperature events in the past 40 years (1970-2010) using the RClimDex package developed by the Expert Team on Climate Change Detection Monitoring and Indices (ETCCDMI), Canada. Temporal variations in trends of occurrence of extreme temperature events were tested with Mann-Kendall trend analysis. The 5-year diurnal temperature range (DTR, difference between monthly mean maximum and minimum temperatures) increased from 7.8 (during 1970-1974) to 9.2 0C (during 2006-2010). The monthly mean maximum temperature increased by 0.035 0C per year. Frequency of occurrence of hot days increased at a rate of 0.56 per cent per year and the highest temperature recorded in a month showed an increase of 0.038 0C per year. As observed, the increasing trends in the occurrence of extreme temperature events may eventually lead to the warming up of the region in future. The study indicates that the projected warming tendency in the traditional rubber growing regions of India may affect the rubber cultivation adversely.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".