Improved Method for Estimating Soil Moisture Deficit in Oil Palm (Elaeis guineensis Jacq.) Areas With Limited Climatic Data
Bibliographic record
Abstract
Widespread water deficit is expected to depress growth and yields of oil palms, which sustain daily losses of water through evapotranspiration of 4-5 mm. While accurate predictions for soil moisture deficit are essential for supplying water to plants through irrigation when rainfall is insufficient, soil moisture deficit is difficult to assess. In Malaysia (as in other oil palm-growing countries) rainfall and rain days are the sole climatic parameters recorded; this limited information is insufficient for reliable estimates of water deficit. This paper reports the adoption of a new method of prediction for soil moisture deficit that takes into account the effective rainfall, evapotranspiration, and a correction factor for the water-holding capacity of the soil. Monthly effective rainfall varied from 11% of gross rainfall to 84%. The number of months with soil water deficit varied from 2 to 12 (mean = 9). Values of water deficit obtained were close to those calculated using the Penman equation, thus validating this method of prediction of water deficit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 | 0.000 | 0.002 |
| 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 teacher head, 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".