Modelling rainfall interception loss in forest restoration trials in Panama
Bibliographic record
Abstract
Abstract A modified Liu analytical model of rainfall interception (Ic) by tree canopies was evaluated using rainfall, throughfall and stemflow data collected from forest restoration trials in the Republic of Panama. The model uses an introduced approach to estimating the water storage capacities of tree boles, which has a more realistic physical basis than earlier iterations of the Liu model. Study species (Acacia mangium, Gliricidia sepium, Guazuma ulmifolia, Ochroma pyramidale, and Pachira quinata) were selected on the basis of differing leaf size and crown characteristics. Significant interspecific differences in both observed and simulated cumulative interception loss were found, with A. mangium intercepting more rainfall than other species. Errors between calculated and modelled cumulative Ic ranged from + 6·3% to + 30·5%, with modelled Ic always being the larger term. During‐event evaporation rates from the study trees were positively related to tree height, crown area, and basal diameter. Crown area and the storage capacity of tree boles were negatively correlated. The results of a sensitivity analysis suggested that the modified model was most sensitive to variations in during‐event evaporation rate. The implications of the model's sensitivity to during‐event evaporation and the importance of this mechanism of interception loss are discussed, while suggestions are provided that may lead to further improvements to the analytical model. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".