Dry pea (<i>Pisum sativum</i> L.) protein, starch, and ash concentrations as affected by cultivar and environment
Bibliographic record
Abstract
Dry pea (Pisum sativum L.) is an important crop in the Northern Great Plains of the USA and Canada. Information on dry pea quality as affected by cultivars and environments is limited. This experiment determined the effects of dry pea cultivars and environments on protein, starch, and ash concentrations. Six dry pea cultivars (‘Arcadia’, ‘Bridger’, ‘CDC Striker’, ‘Cruiser’, ‘Montech 4152’, and ‘SW Midas’) were evaluated in a randomized complete block design with four replications in 22 environments. The results showed that cultivar × environment interaction effects were highly significant on protein, starch, and ash concentration (p < 0.0001). These interaction means, calculated on a dry matter basis, ranged from 145 to 278 g kg−1 seed for protein, 439 to 617 g kg−1 seed for starch, and 10.5 to 31.9 g kg−1 seed for ash. The differences among environmental means were substantial compared with cultivar means. When averaged over environments, ‘CDC Striker’, ‘Arcadia’, and ‘Montech 4152’ produced greater mean protein, starch, and ash concentrations, respectively, than the other cultivars. None of these cultivars simultaneously outperformed the others for protein, starch, and ash concentrations. This may indicate the need to develop cultivars with outstanding qualities across environments to receive satisfy premium end-user quality requirements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".