Mathematical Modeling of Olive Trees Age: Case Study of ‘Mehras’ Variety in Kathraba Village in Karak Governorate
Bibliographic record
Abstract
This study explores methods for estimating the approximate age of Olea europaea (Mehras variety) in Kathraba, Ay District, Al-Karak Governorate, Jordan. Traditional methods for age estimation are often imprecise and time-consuming. Although the Pannelli algorithm is well-established, this research integrates it with mathematical modeling, machine learning, and remote sensing techniques to enhance accuracy and practicality. Data on tree dimensions, environmental conditions, and growth traits were collected and analyzed. Results indicate a strong correlation between physical characteristics and estimated age. Single-trunk trees had a maximum mean estimated age of approximately 1,025 years, with a trunk circumference of 13 meters. Multi-trunk trees showed a slightly higher mean estimated age of around 1,030.5 years, possibly due to the complexity of growth patterns rather than actual age differences. The highest estimated age reached 1,242 years based on basal stump perimeter (maxTD), while the lowest perimeter measurements were recorded below the trunk bio-fraction at height HPM. A significant relationship was observed between base diameter, basal stump measurements, and cumulative branch dimensions with the perimeter (PM) of each trunk. To reduce variability, the arithmetic mean of four age estimates was used as the final age value.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".