Effects pre-harvest hexanal application on fruit market attributes of orange varieties grown in Eastern zone of Tanzania
Bibliographic record
Abstract
The study objective was to determine the effects of field application of hexanal on pre-harvest market attributes of orange (Citrus sinensis L.) fruits. The first factor was hexanal concentration (0.01, 0.02, 0.04% and controls – untreated fruits), the second factor consisted of time of hexanal application prior to fruit harvest (7, 21, 42 and 60 days to harvest) and the third factor was season (1st and 2nd season). Tested orange varieties were Early Valencia (‘Msasa’), Jaffa and Late Valencia varieties. A fruit tree for each orange variety constituted a treatment for hexanal application and time of its application prior to fruit harvest. The results show that hexanal application at 0.01, 0.02 and 0.04% equally improved fruit marketable yields by increasing fruit firmness and number of marketable fruit of Early Valencia, Jaffa and Late Valencia varieties. Number of marketable fruit increased by 34.89%, 34.04% and 42.48% over the controls for Early Valencia, Jaffa and Late Valencia, respectively. Similarly, fruit firmness increased by 11.38, 11.03 and 11.92 N/mm2 over the control for Early Valencia, Jaffa and Late Valencia, respectively. It is recommended that farmers should treat Early Valencia, Jaffa and Late Valencia with hexanal at 0.01% in order to increase marketable yield and fruit quality.
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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".