Methods of calculating growing degree-day based on LR assumption and daily extreme temperatures
Bibliographic record
Abstract
Growing degree-day(GDD) is an important indicator for assessing regional heat resources and timing phenol-stage process,and correct GDD calculation is the basis of water and fertilizer management in agricultural production and accurate simulation and prediction of crop growth and development in crop models.In this paper,two daily average temperature methods of calculating GDD based on assumption of optimized response of growth and development to temperature(OR assumption) and daily extreme temperatures was reviewed,and a growing degree-hour(GDH) method of calculating growing degree-days was introduced.Hourly temperature was estimated by diurnal temperature curve simulated by sine wave based on extreme temperatures.Theoretical argument showed that daily average temperature method may result in an over-or under-estimated GDD,and daily extreme temperatures method may result in an over-estimated GDD;Case study showed the GDH method had the smallest error comparing with the true GDD in three methods.To calculate accurately GDD,GDH method is recommended strongly when GDD is calculated by extreme temperatures based on OR assumption.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".