Investigation of the Optimal Harvesting Time in Some Iranian and Mediterranean Olive Cultivars Based on Their Oil Content and Fatty Acid Compositions
Bibliographic record
Abstract
Harvest time, in olive, is an important key to achieve high quality and quantity of oil with a desired and balanced fatty acid (high oleic acid and low palmitic and linoleic acid) composition. Appropriate harvesting time varies in different locations depending on climatic and agronomic situations and identifying the right time for harvesting may bring about a high quality oil. Thus, the current study was carried out to determine the best harvesting time for two Iranian (Mari and Shenge) and two foreign (Kroniki and Arbequina) cultivars in the region of Tarom, Zanjan, (north of Iran). The results obtained indicated that palmitic, oleic and stearic acid contents decreased and linoleic and palmitic acid contents increased with the progress of ripening. In addition, oil percentage increased with the progress of fruit growth and development. Based on the amount of desired fatty acids, the best harvesting time for the Mari, Kroniki and Arbeqina cultivars, was 180 days after full bloom. It was revealed that the cultivar Shenge is not suitable for oil extraction, due to the low percentage of oleic acids. Therefore, Shenge could be cultivated for producing olive cans rather than olive oil and the best harvesting time for Shenge is 120 days after full bloom.
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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) | 0.000 | 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".