Environmental assessment method for a small-river restoration plan
Bibliographic record
Abstract
Although more than 23,000 river restoration projects have been conducted during the past 15 years in Japan, reliable environmental assessment methods have not yet been identifi ed.The environment of the Kamisaigo River is assessed before restoration.The river had been canalized with concrete revetments, reducing its biological function.In 2007, the Fukutsu City Government initiated a program to restore the environmental quality of the river.The aim of this paper was to determine the best method of assessing the river environment.The fi sh and the physical environment to assess the environmental condition of the river are surveyed.A fi sh index developed by Kyushu University adequately represented characteristics of river health and was used to determine which sites warranted restoration, rehabilitation priorities, and appropriate methods.The authors calculated 14 regionally developed indices using the ecological features of the fi sh at several river sites.The environmental quality of the river varied substantially across seven sampling sites.Using these assessment results, the authors determined the specifi c weaknesses that affected the condition of each site.A restoration and improvement program based on these fi ndings accomplished several goals, including restoring the fl oodplain, incorporating a variety of fl ow rates, and enhancing vegetation for fi sh spawning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".