A comparison of observed and simulated hydrograph separations for a field-scale rainfall-runoff experiment.
Bibliographic record
Abstract
An integrated numerical model of surface-subsurface water flow and solute transport is used to investigate streamflow generation mechanisms operating during a rainfall-runoff experiment. The field site, located at CFB Borden in Ontario, Canada, is approximately 90 m by 18 m and is underlain by a sandy soil exhibiting a distinct capillary fringe. A man-made stream channel along the main axis of the field site lies approximately 1.5 m below adjacent topographic highs. Rainfall applied during the field experiment induced a rapid response in the shallow water table, increasing hydraulic head gradients towards the stream. Surface water is observed to pond on the land surface at and adjacent to the stream channel. Groundwater (pre-event) contributions are interpreted to form up to 37% of streamflow, based both on chemically based hydrograph separations and on groundwater discharge volumes (seepage) calculated using flow nets. Separation of the observed stream discharge is performed using a conservative tracer (bromide) originating in the rainfall. The simulated response of the shallow water table to rainfall is consistent with both theory and observations but suggests that increased subsurface head gradients do not cause significant groundwater seepage. Rather, infiltration rates along the stream axis are reduced, with runoff formed largely by excess rainfall over a dynamic contributing area. Further, the corresponding transport simulations suggest that, despite the rapid, large-scale response of the capillary fringe, rainfall tracer dilution occurs largely by diffusive processes as water flows over the land surface to the stream, over relatively short flow paths, and subsequently down the stream channel. Tracer originating above the initial water table enters the surface water by similar processes, augmenting the small volumes of seepage caused by increased subsurface hydraulic gradients.
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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 | 0.001 | 0.001 |
| 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 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".