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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".