Multiple season-long sprays of ethephon or NAA combined with calcium chloride on ‘Honeycrisp’ apples: I. Effect on bloom and fruit quality attributes
Bibliographic record
Abstract
‘Honeycrisp’ apple trees are highly prone to biennial bearing and predisposed to bitter pit. The hypothesis that tank mix sprays of ethephon (ETH), naphthaleneacetic acid (NAA), and 1-aminocyclopropane carboxylic acid (ACC) combined with calcium chloride (CaCl2) can mitigate these production problems was tested in a 3-yr study. Mature ‘Honeycrisp’ trees were treated with either three or six summer applications of 150 mg L−1 ETH or 5 mg L−1 NAA, all tank-mixed with and without CaCl2, or two or five applications of 150 mg L−1 ACC (without CaCl2). Treatments were applied at 10-d intervals and initiated between 21 and 26 June. All treatments had little effect on enhancing return bloom of ‘Honeycrisp’. NAA, ETH, and CaCl2 all influenced fruit maturity and quality at harvest to varying degrees and across years. Fruit treated with NAA were firmer compared with untreated fruit in 2 out of 3 yr, whereas overall, fruit treated with six sprays of ETH had lower fruit firmness and were more mature. NAA had less influence on fruit quality attributes at harvest than did ETH, and decreased pre-harvest fruit drop (PFD). PFD increased with ETH in 1 out of 2 yr, whereas ACC and NAA both decreased PFD in 1 out of 2 yr. Overall, ETH and NAA, with or without CaCl2, had significant but inconsistent effects on fruit quality and maturity, all dependent on the year and number of applications. Adding CaCl2 decreased fruit firmness in 2 out of 3 yr.
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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 | 0.001 | 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".