Multi-Level Temporal Variation of Sap Flux Densities in Oil Palm
Bibliographic record
Abstract
Oil palms (Elaeis guineensis Jacq.) are increasingly cultivated throughout the humid tropics and are reported to have high transpiration rates. A potential contribution of stem water storage to transpiration has been discussed in previous studies. We assessed the water-use characteristics of oil palms at different horizontal and vertical positions in the plant by using three sap flux techniques, i.e., thermal dissipation probes, the heat ratio method and heat field deformation sensors. In a radial profile of the stem, sap flux densities were low at the outer margin, increased to 2.5 cm under the bark and remained relatively high to the innermost measured depth at 7.5 cm. In a vertical profile of the stem and with further sensors in leaf petioles, we found only small time lags in sap flux densities. Time lags along the flow path are often used for analyzing the contribution of water storage to transpiration. Thus, the small observed time differences in our study would leave only little room for the contribution of water storage to transpiration. However, water storage might still contribute to transpiration in ways that are not detected by time lag analysis. Such mechanisms may be explored in future studies.
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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 | 0.001 | 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) | 0.001 | 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".