Spatial Prediction Model of Plant Water Status of an Olive Orchard (Olea europaea L.) cv. Arbequina Under Semiarid Conditions in the Central Valley of Chile
Bibliographic record
Abstract
The Olive (Olea europaea L.) is a typical fruit tree of Mediterranean areas characterized by high-quality oil production and high tolerance to water deficit. Due to worldwide water scarcity in Mediterranean regions, it becomes indispensable to monitor plant water status, in example, through xylem water potential (Ψx). Unfortunately, measurement is difficult to perform with high spatial resolution at field scale (> 50 measurements per hectare), due to the large amount of manpower required in the prosses which turned this technique into a high-cost solution. This situation drastically hinders its applicability in large production areas. Thus, the objective of this research is implementing a spatial prediction model of plant water status in an olive orchard, using a single Ψx measurement performed in a reference site over the orchard. The experimental site was established in 2.2 hectares of commercial olive trees in the Pencahue valley located in the Maule region (Chile) during the 2013/14 growing season. Measurements of Ψx were performed at key phenological stages of olive trees. The proposed methodology allowed to estimate the behavior of Ψx in unsampled olive trees from reference site measurements, with an average spatial error less than ±0.6 MPa and correlation of 0.8 (R2) ratifying the high spatial dependence between different sites sampled at field scale. Therefore, distribution of spatial variability would be adequate for the application of irrigation in homogeneous management zones, facilitating water management practices in clearly identified zones within the olive orchard under study.
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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 | 0.001 | 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".