Pre-harvest field application of enhanced freshness formulation reduces yield loss in orange
Bibliographic record
Abstract
Abstract Background Orange (Citrus sinensis L.) production in Tanzania is constrained by several pre-harvest factors that include pests. Hexanal, sprayed as Enhanced Freshness Formulation (EFF) is a relatively new technology that has been reported to reduce pre-harvest loss in fruits. However, the effects of hexanal on pre-harvest yield loss of orange are not known. We studied the effects of hexanal as EFF on yield losses of three sweet orange cultivars namely, Early Valencia, Jaffa, and Late Valencia. Factorial experiments tested the effects of EFF concentration, variety, and time of EFF application on number of dropped fruit, percentage of non-marketable fruit and incidence of pest damage. Results Results showed significant negative correlation (p < 0.001) between EFF and the percentage of dropped fruit, non-marketable yield, and incidence of pest damage. An increase in hexanal concentration by 1%, is expected to reduce number of dropped fruit by 50, percentage of non-marketable by 35.6, and incidences of pest damage by 36.5% keeping other factors constant. Results also show significant association (p < 0.001) between time of hexanal application and non-marketable yield. Percentage of dropped fruit is expected to increase by 1 for each day away from harvest, keeping other factors constant. Conclusion Pre-harvest application of hexanal as EFF can significantly reduce number of dropped fruits, percentage of non-marketable fruit and incidence of pest damage.
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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) | 0.002 | 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".