Statistical modeling and forecasting of fruit crop phenology under climate change
Bibliographic record
Abstract
Phenology, the study of the association between biological development stages and variations in climate, has greatly increased in importance because of concerns arising from climate change. This paper presents a general stochastic approach to the modeling of the relationship between phenological events and climate variables, and gives a prediction method based on this approach to provide full predictive distributions for future events. The proposed methods are then applied to the modeling and prediction of the bloom dates of six high‐valued fruit crops. In particular, we use our approach to explore how the bloom dates are related to the accumulation of growing degree days, to provide a sensible estimate of an important parameter T b a s e in phenological study, and to assess the prediction of bloom dates with a leave‐one‐out procedure. Most importantly, the impact of future climate change on bloom dates is studied with temperature outputs from well‐established coupled global climate models under a high greenhouse gases scenario. Copyright © 2014 John Wiley & Sons, Ltd.
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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".