Relationships among growing degree-days, tenderness, other harvest attributes and market value of processing pea (<i>Pisum sativum</i> L.) cultivars grown in Quebec
Bibliographic record
Abstract
In Quebec, grower income from processing peas is a function of yield and tenderometer reading. If peas are harvested early, the yield is poor, but the overall quality is superior, as indicated by lower tenderometer readings. Later harvests result in g reater yields but reduced quality (higher tenderometer readings). A better understanding of the relationship between harvest time, yield and quality is needed. In this study, the relationships between yield, tenderometer readings, size distribution and grain moisture content were examined as a function of growing degree-days (GDD) and production year for pea cultivars of different seed size categories. Yield and harvest attributes (tenderometer readings, seed size distribution and moisture content) were highly season dependent and their rates of change over the course of the harvest period also varied with the cultivar and year. Quality declined rapidly once the crop reached optimal maturity, while yield increased in a less predictable manner. Consequently, it was difficult to identify a harvest time that would consistently maximize grower returns. The highest income was generally not obtained at the optimal tenderometer readings presently used by the industry. Key words: Maturity, harvest date, climate, fresh matter yield, tenderometer, pea size
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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 | 0.001 |
| 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) | 0.001 | 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".