Noor hard red spring wheat
Bibliographic record
Abstract
Noor hard red spring wheat (Triticum aestivum L.) was developed using a modified bulk breeding method at the University of Alberta, Edmonton, AB Canada. Noor is an apically awnletted, hollow-stemmed line with a combination of high yield potential, good lodging tolerance, and medium maturity. During the three years (2016–2018) of evaluation in the Parkland Wheat Cooperative test, Noor yielded 12% higher than the mean of all the checks and matured similarly to Carberry and Glenn, but 3.1 and 2.6 d later than AC Splendor and Parata, respectively. Noor was 94.9 cm tall, shorter than AC Splendor (98.4 cm), similar in height to Glenn (94.0 cm) and Parata (93.5 cm), but taller than Carberry (86.0 cm). The lodging score of Noor (2.1) was lower than Parata (3.0) and AC Splendor (3.0), but similar to Carberry (2.3) and Glenn (2.1). The test weight (kg·hL−1) of Noor (80.0) was higher than AC Splendor (78.6), similar to Carberry (80.2) and Parata (80.4), but lower than Glenn (82.2). Grain weight (35 g) and NIR protein of Noor (14.1%) was lower than all the checks. Overall, Noor was rated resistant (R) to the prevalent races of leaf, stem, and stripe rusts during the three years of testing. Noor was rated intermediate (I) to common bunt and Fusarium head blight. Three years of end-use quality evaluation indicated that Noor is acceptable for the Canada Western Red Spring class, with fewer flags.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".