The role of pre‐event canopy storage in throughfall and stemflow by using isotopic tracers
Bibliographic record
Abstract
ABSTRACT Stable isotopes can be a valuable tool for tracing the redistribution, storage, and evaporation of water associated with canopy interception of rainfall. Isotopic differences between throughfall and rainfall have been attributed to three mechanisms: evaporative fractionation, isotopic exchange with ambient vapor, and temporal redistribution. We demonstrate the potential importance of a fourth mechanism: rainfall mixing with water retained within the canopy (in bark, epiphytes, etc.) from prior rain events. Amount and isotopic composition (18O and 2H) of rainfall and throughfall were measured over a 3‐month period in a Douglas‐fir forest in the Cascade Range of Oregon, USA. The range of spatial variability of throughfall isotopic composition exceeded the differences between event‐mean isotopic compositions of rainfall and throughfall. Inter‐event isotopic variation of precipitation was high and correlated with the isotopic deviation of throughfall from rainfall, likely related to a high canopy/bark storage capacity storage bridging events. Both spatial variability of throughfall isotopic composition and throughfall–precipitation isotopic differences appear to have been controlled by the temporally varying influence of residual precipitation from previous events. Therefore, isotopic heterogeneity could indicate local storage characteristics and the partitioning of flow‐paths within the canopy. Copyright © 2013 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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".