Patterns of water movement on a logged Gray Luvisolic hillslope during the snowmelt period
Bibliographic record
Abstract
Hillslope flow processes during the snowmelt period were studied at a logged site in the Boreal Plain Ecozone of western Canada. Fine-textured subsoils of Gray Luvisolic soils and soil frost were hypothesized to reduce infiltration capacity and promote interflow. Liquid soil water content, saturated flow through upper horizons, and soil temperature were monitored by Time Domain Reflectometry probes, zero-tension flow collectors, and thermocouples, respectively, on a 0.5-ha site with a 13% slope. Soil water content increased abruptly during snowmelt while soil temperature in the upper 65 cm was near 0°C, indicating that infiltration capacity was high despite frost. Mineral soil thawed 2 wk after snowmelt. Less than 0.1 mm of the 87 mm snow water equivalent became interflow. Size and timing of interflow events were variable and related to increased soil water content. The largest event occurred during soil thaw, and contributed 84% of total interflow. The lower Ae horizon was the preferred route for this flow, suggesting that the flowpath was not influenced by frost. Low pre-melt soil moisture probably reduced interflow volume. Interflow in Gray Luvisols is likely an infrequent happening due to high profile moisture storage capacity and rare development of the necessary saturated conditions. Key words: Snowmelt, infiltration, frozen soil, boreal, hydrologic flowpath, Luvisol
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".