Identification of critical source areas which contribute nutrients to snowmelt runoff
Bibliographic record
Abstract
The presence of nutrients in snowmelt runoff from agricultural watersheds has been reported by previous studies. However, no study has answered the most important question “what areas of the watershed contribute nutrients to snowmelt runoff?” or addressed the factors that control snowmelt runoff water quality. This study was designed to (1) find the areas that contribute nutrient to snowmelt runoff (termed as critical source areas, CSA), and (2) understand the source and transport factors that control the snowmelt runoff water quality in the Canadian prairies. The findings of this study will provide vital information to understand snowmelt runoff water quality and for sustainable management of soil nutrients and snowmelt runoff water quality in the Canadian prairies. Source and transport factors and snowmelt runoff water quality were studied for two years on shoulder, backslope and footslope landform segments. The distribution of fall soil nutrients in the top 5 cm soil layer (available soil P [ASP], nitrate [NO3-] and ammonium [NH4+]), snow depth, snow water equivalent (SWE), snowmelt runoff and snowmelt runoff water quality (total P [TP], total dissolved P [TDP], NO3-N and sediment) were studied using closed and open plots placed on each landform segment. The influence of source and transport factors was evaluated in relation to snowmelt runoff water quality. The ASP had a distribution pattern of backslope < shoulder < footslope in 2003 before manure application (bma) and shoulder = backslope = footslope in 2004. The NO3- distributed as shoulder = backslope = footslope in 2003 (bma) and shoulder < backslope < footslope in 2004. However, NH4+ had a stable distribution of shoulder = backslope < footslope in 2003 bma and in 2004. The pre-melt SWE increased in the down slope direction having the lowest in the shoulder and backslope and the highest in the footslope in 2005. The average daily snowmelt runoff from 1 m2 plots did not vary between the shoulder and the backslope. Infiltration was dominant in 2004 while runoff was dominant in 2005. Of the three landform segments, the shoulder was the greatest contributor of runoff to the depression. The backslope contributed the least. Hog manure injection did not seem to influence snowmelt runoff water quality. Most nutrients and sediments were from the land surface. Analysis revealed that fall soil nutrient concentrations were not a dominant factor controlling the nutrients in the snowmelt runoff in this site. However, snowmelt runoff volume controlled snowmelt runoff water quality. Snowmelt runoff water quality did not vary between the landform segments. However, as a result of the dominance of shoulder in this landscape, the total transport of nutrients and sediment was the highest from shoulder. Where landform characteristics are similar to the study watershed, it may be argued that all landform segments are CSA. Runoff volume is the most influential factor in determining the importance of CSA and controlling the snowmelt runoff water quality.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".