Spatiotemporal variability of minimum runoff generating areas: a field investigation
Bibliographic record
Abstract
Past studies described several dominant factors responsible for minimum runoff-generating areas (MRGAs) which are seldom backed by field observations. Here, soil moisture and runoff data for 45 rainfall events were collected from a small (21.62 ha) agricultural watershed in Ontario, Canada, using a remotely operated wireless sensor network system. The relationship developed between MRGA and basin moisture index (M) indicated that MRGAs are insensitive up to the threshold M value of 25 cm. A higher M leads to an exponential increase in the MRGA. Various factors (e.g. rainfall amount) affecting the spatiotemporal variability of MRGA were then identified. Statistical analyses (e.g. F-test) indicated a strong seasonal influence and showed that MRGA during spring, summer, and fall seasons was 34.3%, 8.0%, and 13.6%, respectively. The relationship of MRGA with rainfall amount, initial soil moisture content and rainfall intensity was the strongest for the summer season followed by fall and then spring.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".