Rainfall probability during dormant and growing seasons of apple in Himachal Pradesh
Bibliographic record
Abstract
Variation of seasonal rainfall and probabilities of occurrence of assured weekly rainfall provide useful information for efficient agricultural management. In the present study, seven stations of Himachal Pradesh have been selected for the analysis of the rainfall data. In general, the station Dalhousie received the higher rainfall during both the growing and dormant seasons along with annual rainfall. Probability percentage of receiving 10 mm, 20 mm, 50 mm and 75 mm rainfall have been computed for standard weeks during growing and dormant seasons at four stations Katrain, Bajaura, Mashobra and Nauni (Solan). A critical examination for spatial and temporal probability distribution revealed that high rainfall belt is located in the north western part of the state. For better apple production, monsoon rainfall plays significant role. It not only moderates the temperature during summer but also recharges the muchneeded moisture, which almost depletes by mid June. Well distributed winter rains and early snowfall before January are beneficial in providing chilling, optimal flowering and good fruit set.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".