Bibliographic record
Abstract
In order to clarify the fundamental mechanisms of the rainfall-runoff processes, many field observations and experiments have been conducted so far, and the development of a rainfall runoff model (hereinafter called a model) has been advanced.Influences of soil physical properties such as permeability and topographic characteristics such as slope gradient are considered in theoretical formula or governing equation as an important parameter in determination of runoff generation along hillslope.Previous studies have shown that surface runoff decreases (infiltration rate increases) with increasing surface cover ratio and amount of surface cover materials.On the other hand, it was shown that surface runoff increases as the slope gradient increases.Fundamental relationships observed by those previous studies can be found in numerous classical/present researches published in academia.However, recent research suggested that the effect of surface cover materials and slope gradient on rainfall runoff processes depend on the soil type and experimental/observational conditions in each study.From these reasons, this study investigated effects of surface cover and slope gradient on rainfall runoff processes for three different soil types: namely, Japanese forest soil under temperate climate, sandy loam and loess by East Asian monsoon area, and purple soil in semi-arid area.In chapter 2, the relation among infiltration rate, surface cover material, and physical parameters of surface soil such as hydraulic conductivity and texture was investigated in Japanese cedar and Hiba arborvitae plantations in Ishikawa prefecture.The measured maximum infiltration rates (hereinafter called a FIRmax) under simulated rainfall condition for the Japanese cedar and Hiba arborvitae stands were in ranges of 141.9 to 562.3 mm/h and 93.3 to 641.0 mm/h, respectively.Different from the results of previous studies, there was no significant relationship observed between the infiltration rate and the surface cover condition.Furthermore, hydraulic
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.014 |
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".