Investigation of the contribution of snowmelt to subsurface drainage from croplands in eastern Canada
Bibliographic record
Abstract
Abstract. Late winter / early spring has been recognized as a critical period for subsurface drainage and associated N and P losses in croplands in cold climate regions in North America and Europe due to the melt of snow cumulated across the winter as well as rainfall events occurring in the winter/spring period. There are limited studies quantifying the contribution of snowmelt to subsurface drainage during spring, despite the drastic difference in underlying particle detachment and transport process from rainfall. This study aimed to investigate the contribution of snowmelt and winter/spring rainfall to the tile discharge in cropland located in Quebec and Ontario, using the Root Zone Water Quality Model-SHAW (RZ-SHAW) model. The RZ-SHAW model was calibrated and validated against the measured snow depth, and tile drainage flow data from 2018 to 2022 for cropland in southern Quebec and from 2000 to 2003 for cropland in Ontario. The snowmelt contribution was determined from the daily snow cover outflow after the energy and mass balance calculation of the snowpack. The snowmelt was simulated to contribute 47% of the total drainage for the cropland in Quebec and 35% in Ontario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".