Improving the Prediction of Processing Pea Maturity Based on the Growing-degree Day Approach
Bibliographic record
Abstract
The heat-unit system, involving the sum of daily mean temperatures above a given base temperature, is used with processing pea ( Pisum sativum L.) to predict relative maturity during the growing season and to schedule planting dates based on average temperature data. The Quebec pea processing industry uses a base temperature of 5 °C to compute growing-degree days (GDD) between sowing and maturity. This study was initiated to verify if the current model, which uses a base temperature of 5 °C, can be improved to predict maturity in Quebec. Four pea cultivars, `Bolero', `Rally', `Flair', and `Kriter', were grown between 1985 and 1997 on an experimental farm in Quebec. For all cultivars, when using a limited number of years, a base temperature between 0.0 and 0.8 °C reduced the coefficient of variation ( cv ) as compared with 5.0 °C, indicating that the base temperature used commercially is probably not the most appropriate for Quebec climatic conditions. The division of the developmental period into different stages (sowing until emergence, emergence until flowering, and flowering until maturity) was also investigated for some years. Use of base temperatures specific for each crop phase did not improve the prediction of maturity when compared with the use of an overall base temperature. All years for a given cultivar were then used to determine the base temperature with the lowest cv for predicting the time from sowing to maturity. A base temperature from 0 to 5 °C was generally adequate for all cultivars, and a common base temperature of 3.0 °C was selected for all cultivars. For the years and cultivars used in this study, the computation of GDD with a base temperature of 3 °C gave an overall prediction of maturity of 2.0, 2.4, 2.2, and 2.5 days based on the average of the absolute values of the differences for the cultivars Bolero, Rally, Flair, and Kriter, respectively.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".