Pre-harvest Sprouting Tolerance of Triticale Genotypes in Brazil
Bibliographic record
Abstract
Pre-harvest sprouting (PHS) represents one of the main factors, which causes yield, technological and physiological losses in triticale seeds (X Triticosecale Wittmack). This work aimed to rate the variability and identify potential pre-harvest sprouting tolerant sources in triticale genotypes. Based on that, 32 triticale and three wheat genotypes were sown in 2016, 2017 and 2018 growing seasons, in Londrina-PR, Brazil. After the ears harvesting, these were submitted to simulate raining, for sprouting induction, through nebulization in a greenhouse. After nebulization, ears were sun dried, later hand threshed to determain, grain germination percentage (GERM) and hectoliter weight (HW). Additionally, it was determined grains HW of ears, whom were not submitted to nebulization, as well as the whole meal flour falling number (FN). The experiment design was completely randomized design, with two replications, and the experimental unit was made of 20 ears. The data collected were subjected to analysis of variance (ANOVA) and Scott-Knott test. Frontana, ND 674 and Quartzo are a source of tolerance to PHS in wheat. In triticale genotypes, genetic variability was observed for GERM, FN, and HW prior and after nebulization. The triticale genotypes BRS Netuno, BRS Saturno, TCL 15116, X 092181, Tiguera 1 and Tiguera 8, where tolerant towards PHS.
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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) | 0.000 | 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".