Effect of the Planting Location (Elevation) on the Composition of Fatty Acids in Olive Oil
Bibliographic record
Abstract
In Jordan there are different environments and altitudes that have an effect on olive oil properties and fatty acid composition, which therefore affect its quality. Taking into account that there are no previous similar studies in this field, this study investigates the ratios and composition of the main fatty acids in mixed olive oil from different areas in Jordan (above and below the sea level); namely the Jordan Valley, Subaihi, Kufranja and Madaba areas where the elevation ranges between (-230 m) below sea level and (+785 m) above sea level. The study measures the impact of the location of olive orchards on the fatty acid composition of olive oil and of its quality. Three olive oil samples were collected from the “Nabali Muhasan” and “Baladi” varieties from each area. They were analyzed in the National Agriculture Research Center (NARC) laboratories using GAS Chromatography. The results showed that the ratios of fatty acids are significantly affected by the degree of above elevation of the olive trees sea level. This confirms the impact of the geographical and climatic environment, which is reflected on the olive oil quality due to differences in the ratios of fatty acids in the four planting areas. This shows that the mean concentration of the fatty acids in the olive oil in these four planting areas is not equal. In general, there is a significant difference in the fatty acid concentrations compared to the standard concentration because of the planting location of olive trees, given that olive oil fatty acids in each location work together in a balanced integrative relationship.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".