Cause-and-Effect Relationships in Dry Beans Cultivars Yield Components Under Crop-Livestock System Management
Bibliographic record
Abstract
In order to identify the contribution of yield components to the final yield of dry beans, two widely adopted cultivars (IAC Milênio, and IPR Tuiuiú) were evaluated under nitrogen fertilization levels (0, 50, 100 and 150 kg N ha-1) in a crop-livestock system. Experiments were conducted under a randomized complete block with three replications design during two years (2016 and 2017) which were split in two cropping phases. A subplot factorial scheme of grazing heights by nitrogen fertilization (grains crops or pasture) was used. Dry beans crops were fertilized during the summer and yield components evaluated along with yield measurements. Descriptive and Pearson’s correlation coefficient analyses were performed and followed by path analysis to explain the interrelationship between yield components (explanatory traits) and yield (main trait). For IAC Milenio in 2016 cultivated as a second crop the main trait influencing yield the number of seeds per plant through direct and indirect effect of other traits, regardless of the topdressing nitrogen level applied. In 2017, a greater number of significant correlations was found for the IPR Tuiuiú cultivar. The number of pods per plant was the trait that affected yield most all nitrogen levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".