Transition of Land Cover Characteristics at Wild-Fired Watershed
Bibliographic record
Abstract
Wild fires in a mountain area cause severe runoff. The runoff causes secondary mal-effects such as soil erosion and environmental contamination. Korea had suffered from serious soil yield problems at Imha reservoir in 2003. The muddy flow in the reservoir lasts for an years at that time and the water resources problem had prevailed around the watershed. But there was no reliable method in predicting the amount of soil yield and developing count measures against soil erosion. The goal of this research is to find the sustainability transition of land cover characteristics in a wild-fired watershed. For the success of this research, experimental watershed which had suffered from wild-fires was operated last five years. With the collected field data, the transition of land cover characteristics of watershed was analyzed. It was found that the land cover factor was increased about one hundred times at first year after the wild fire. Then it decreases constantly until it remains stable condition which is reached at fourth year after wild-fires.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".