Pinto bean response to seeding rate and herbicides
Bibliographic record
Abstract
Abstract Field experiments were conducted in 2016 and 2017 to evaluate the effects of seeding rate and herbicide programs on weed control and pinto bean yield under irrigation. The experiments comprised a 5 × 5 factorial randomized complete block design with five replications. The weed control treatments comprised a nontreated control, hand-weeded control, EPTC + ethalfluralin PRE, EPTC + ethalfluralin PRE followed by (fb) dimethenamid-P POST at V1, and EPTC + ethalfluralin PRE fb bentazon/imazamox POST. There were five seeding rates ranging from 247,000 to 494,000 seeds ha–1 planted in 19-cm rows. Weed biomass was reduced by 6 kg ha–1 with every additional 1,000 seeds ha–1. EPTC plus ethalfluralin fb either dimethenamid-P or bentazon plus imazamox reduced weed biomass by at least 29% compared to the nontreated control. There was a significant effect of weed control treatment on pinto bean yield (P = 0.0004). However, there was no significant seeding rate (P = 0.42) or seeding rate–by–weed control interaction effect on pinto bean yield (P = 0.38). Pinto bean yield ranged from 3,080 kg ha–1 in the nontreated control to 4,740 kg ha–1 hand-weeded treatment. Increased seeding rate in narrow rows is a cultural practice that can improve weed control in pinto bean but may not necessarily increase yield.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".