Runoff reduction by forest growth in Hiji River basin, Japan / Diminution de l'écoulement causée par la croissance forestière dans le bassin versant du Fleuve Hiji, Japon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Forest growth unfavourably reduces low flows and annual runoff in a basin in Japan.Annual precipitation and runoff of the watershed are summarized from observed daily rainfall and discharge, and annual evapotranspiration is estimated from the annual water balance.The water balance analysis shows obvious trends: reduced annual runoff and increased evapotranspiration over a 36-year period when forest growth increased the leaf area index.Between two periods, 1960Between two periods, -1969Between two periods, and 1983Between two periods, -1992, mean annual runoff decreased 11%, from 1258 to 1118 mm, due to a 37% increase in evapotranspiration (precipitation minus runoff) from 464 to 637 mm.This increase in evapotranspiration cannot be attributed to changed evaporative demand, based on climatic variability over the 36-year period of record.Flow duration curves show reduced flows in response to forest growth.In particular, they suggest stronger absolute changes for higher flows but stronger proportional changes for medium and lower flows.A distributed model is applied to simulate the influences of five scenarios based on a 30% change in leaf area index and 5% change in soil storage capacity.From the simulation results, canopy growth appears to contribute much more to flow reduction than changes in soil storage capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it