Modelling canopy interception loss from a Madrean pine‐oak stand, northeastern Mexico
Bibliographic record
Abstract
Abstract Throughfall associated with 34 rainfall events was measured from Year Day 1, 1999 to Year Day 184, 2001 within a pine‐oak stand of the central Sierra Madre Oriental mountain range of northeastern Mexico. Throughfall and canopy interception loss accounted for 83·6 ± 2·1% and 15·8 ± 1·8% (significance level, α, = 0·05) of the 691·8 mm cumulative rainfall input, respectively. A reformulated Gash analytical interception loss model and the Liu analytical model run with predetermined model parameters derived from the literature underestimated cumulative interception loss by 34·4%, and 37·1%, respectively. When the models were run with study period derived parameters the reformulated Gash model overestimated the observed interception by 3·0%, while the Liu model underestimated observed cumulative interception by 0·8%. Although good agreement between observed and estimated cumulative canopy interception loss was found using the analytical models with study period derived parameters, relatively poor agreement was found at the rainfall event scale. Although the analytical versions of the reformulated Gash and Liu models provided similar results, the Liu model is recommended for further application in the study area since it requires less data input and follows an exponential wetting of the canopy approach, something that the data collected during the study supports. Future work regarding the Liu model is discussed. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.000 | 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 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".