Influence of the Period of Peach Tree Chemical Thinning on Fruit Quality
Bibliographic record
Abstract
The need to decrease production costs along with the lack of man power in the countryside has asked for cultural practices which lead to these factors, such as thinning. Thus, chemical thinning has been studied as an alternative to fruit manual thinning. Therefore, this study aimed at evaluating the quality of peach tree fruits after chemical thinning with metamitron at different time periods in the south of Brazil. The experiment was carried out in a commercial peach tree orchard with cultivars ‘Maciel’ in Morro Redondo, Rio Grande do Sul state, Brazil, from 2015 to 2016. Treatments consisted in the application of metamitron (doses of 200 mg L-1) on the 20th, 30th, 40th, 50th and 60th day after full bloom (DAFB) and manual thinning on the 40th DAFB. Epidermis color, pulp firmness, ripeness index, soluble solids, titratable acidity, juice pH, total phenolic compounds and antioxidant activity were evaluated. The application of metamitron in peach tree thinning did not affect peach color. Changes in the other variables related to the quality of fruits depended on the application period of metamitron in the thinning process and between crops.
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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 | 0.000 |
| 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".