Micro-Droplet Flux in Forest and its Contribution to Interception Loss of Rainfall – Theoretical Study and Field
Bibliographic record
Abstract
A new approach to explain forest interception was proposed by introducing micro-droplets of crushed raindrops during rainfall. The aerodynamic diffusion and transfer of both vapour and micro-droplets from canopy to upper air were described and calculated, and proposed formulas applied to eight rainfall events at the Okunoi Experimental Station, Tokushima, Japan. Contributions from droplet transfer were 0.9-58.2 times of contributions from vapour transfer, taking a majority portion in total interception loss. Accounting only the vapour transfer or evaporation loss as estimated by Penman equation was not able to account for actual interception loss. The micro-droplet flux component took major portion in the two heavily rained events, and completely made up the interception as happened in October 2004. The droplet flux could accommodate a high interception rate, even when the air was nearly vapour-saturated and vapour flux was zero. This approach provided a new explanation to extraordinarily high interception rates.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".