Bibliographic record
Abstract
Aluminum toxicity is a growth-limiting factor for crop production in the south brazilian soils. This toxicity can be avoided by raising pH up to 5,5 - 6,0 with aglime or by growing Al tolerant cultivars. A oat breeding program for aluminum tolerance has been carried out since 1986 at the University of Passo Fundo by using segregating populations from F2 to F5, at acid soils, on field conditions (Floss et al., 1991). Floss et al. (1993) reported results about a screening for Al tolerance where 156 genotypes of oats (Avena sativa L.) were grow in nutrient solutions (growth chamber). On of the most Al tolerant genotype obtained in thi screening was the cultivar Steele, from USA. In field conditions and acidic soils, many tolerant lines were selected and evaluated for other important traits, as grain yield, test weight, disease resistance, plant height, days from emergency to flowering and to maturity, and grain quality. One line from this segregating populations, line UPF92Al300, introducted in 1992 by Quaker Oat Nursery (cross QR=981=UPF80197/(X2082- 2/CI8428)/Steele, made in College Station by Milton McDaniel, was selected as aluminum tolerant and evaluated in test trials from 1998 to 2001. This line was released in 2002, by the University of Passo Fundo (UPF), named UPFA 20-Teixeirinha. The average performance obtained at 14 different fields, from 1998 to 2002 was: yield = 3049 kg ha-1; test weight = 50 kg hL-1; thousand grain weight = 36 g; days to heading =87; days to maturity=131; height =103 cm; % of grains wider than 2mm =84; % of groats =71; and moderate resistance to leaf and stem rust. Floss,E.L. et al. Primer congreso sudamericano de avena, Tres Arroyos/ Argentina, 1991, Resumos. p.15; Floss, E.L. et al. In:V International Oat Conference, 1996. Proceedings. Saskatoon, 1996,p. 623-625.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".