Biometric Indexes in the Early Selection of Potassium Use Efficient Sugarcane Genotypes
Bibliographic record
Abstract
Brazil is the world’s largest producer of sugarcane (Saccharum officinarum). In this context, in addition to the already extensive areas occupied with sugarcane crops, new areas, characterized as having low natural soil fertility, have been incorporated into the production system, showing that the new sugarcane genotypes that are efficient in the use of mineral nutrients in the soil they must be selected for use in these areas. Thus, the objective with this work was to evaluate the feasibility of using biometric indexes in the early selection of potassium (K) use efficient sugarcane genotypes. For this, four sugarcane genotypes were submitted to five doses of K, evaluating the possibility of selection during the initial phases of the crop (at 5, 8 and 14 months of age). The RB92579 genotype was the most efficient in the use of K for stem dry matter, showing that it is possible to select efficient genotypes in the use of K using the stem dry mass or efficiency in the use of K as indexes already at 8 months after planting in sugarcane, but provided that they are tested under conditions of low K availability in the soil, that is, without adding fertilizers to the soil.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".