Runoff, sediment, organic carbon, and nutrient loads from a Canadian prairie micro-watershed under climate variability and land management practices
Bibliographic record
Abstract
Steppler subwatershed in southern Manitoba of Canada based on 11 years of field monitoring data collected at nine stations. Results showed that the nutrient losses were very small because of the implementation of multiple BMPs in the study area. However, a high spatio-temporal variation of runoff and water quality parameters was found for the nine fields within the subwatershed. The average runoff coefficient was 0.19 at the subwatershed outlet with sediment, suspended particulate carbon, total nitrogen, and total phosphorus losses of 73.8, 6.10, 4.54, and 0.76 kg/ha respectively. Spring snowmelt runoff was about 74.5% of the annual runoff at the subwatershed outlet, while for sediment, suspended particulate carbon, total nitrogen, and total phosphorus, the proportions were 61.1%, 63.6%, 74.9%, and 81.2% respectively during the monitoring period, which suggests that BMPs designed for reducing nutrient loadings from snowmelt runoff would be more effective than BMPs designed for reducing pollutant loading from rainfall storms in the study area. Research findings from this study will benefit the enhancement of current BMPs and the development of new BMPs in the region to minimize soil and nutrient losses from agricultural fields and improve water quality in receiving water bodies.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".