Fall and Winter Temperatures, Together with Spring Temperatures, Determine the First Flowering Date of Prunus armeniaca L.
Bibliographic record
Abstract
Chilling and spring temperature accumulation are both considered key factors determining the timing of the spring bloom in many flowering plants. The accumulated developmental progress (ADP) method predicted the first flowering date (FFD) of a species of Rosaceae well in a previous study. However, whether this approach can be applied to other species, and whether the prediction errors in FFD based on the ADP method can be further accounted for by fall and winter temperatures (FWTs), remains unknown. The ADP method and two others were tested using a 39-year apricot FFD data series. The goodness of fit obtained with each method was assessed using the root mean square error (RMSE) between the observed and predicted FFDs. We used the residuals obtained using the ADP method as a response variable to fit generalized additive models (GAMs) including six FWTs as predictors. The GAMs generated based on different combinations of predictors were compared using Akaike's information criterion (AIC) to test whether using FWTs can reduce prediction error. The ADP method had the lowest RMSE, which equaled 3.0904 days. Together, the number of cold days, the number of chilling hours, the mean value of the daily maximum temperatures, and the mean value of the daily mean temperatures from 1 November of the preceding year to the starting date accounted for 96% of the deviance in the residuals obtained using the ADP method. Including these predictors reduced the RMSE to 0.6162 days. The ADP method is a valid technique to quantify the effect of spring temperatures from a given starting date on the FFD. The FWTs and the number of cold days can also influence the FFD. The present work provides evidence that FWTs including daily maximum temperatures and spring mean temperatures together determine the FFD of apricot.
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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".