Diurnal spray timing does not affect the thinning of apples with carbaryl, benzyladenine, and napthaleneacetic acid
Bibliographic record
Abstract
Inclement weather during fruit set of apples [Malus sylvestris (L.) Mill var. domestica (Borkh.) Mansf.] can restrict an orchardist's ability to apply chemical thinners in a timely fashion. While recommendations for the timing of chemical thinners are very specific to the phenological stage of development and the environmental conditions following spray application, few studies have addressed the influence of diurnal timing and environmental conditions at the time of application on treatment efficacy. This 2-yr study was designed to determine the effect of diurnal spray timing and product on the thinning of mature Empire/M.26 apple trees. Treatments of 10 mg L-1 1-napthaleneacetic acid (NAA), 75 mg L-1 N-(phenylmethyl)-1H-purine-6-amine (6-benzyladenine) (BA), and 750 mg L-1 1-napthyl-N-methylcarbamate (CB) were applied at approximately 6-h intervals over a 24-h period when the largest fruitlets were 10–12 mm in diameter. Environmental conditions at the time of spraying in both years ranged from very cool (5°C) and humid (> 90% RH) causing slow drying times (> 180 min), to warm (25°C) and dry (35% RH) with rapid drying times (< 15 min). Fruit thinning was consistently influenced by the chemical thinning product, but was unaffected by diurnal timing in both years. This work strongly indicates that choosing a specific time of day or ideal temperature and humidity conditions for spraying is not a major factor influencing thinning results if the combination of fruitlet development stage and chemical product is otherwise suitable. Key words: Malus, spray conditions, chemical thinning, crop density
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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 | 0.000 | 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".