SEASONALLY DRIVEN VARIATION IN SPATIAL RELATIONSHIPS BETWEEN AGRICULTURAL LAND USE AND IN‐STREAM NUTRIENT CONCENTRATIONS
Bibliographic record
Abstract
ABSTRACT Geographic information system (GIS) based distance weighted models were applied to determine critical areas of agricultural influence in nine agriculturally dominated, prairie subcatchments in southern Manitoba, Canada. Models were generated using a range of coefficients to represent nutrient overland and in‐stream attenuation between agricultural source areas and stream sampling stations. Coefficients were also used to represent increased attenuation during overland travel through areas with natural vegetation. Water samples collected at intervals throughout the open water season were used to establish associations between areas of influence and in‐stream total nitrogen and phosphorus concentrations in each season and under different flow conditions. Critical areas of influence varied seasonally with areas of influence expanding with individual rainfall events. Inclusion of natural vegetated areas on the landscape resulted in substantial increases in model power for only one scenario. Agriculture in areas within approximately 100 m of the stream channel appears to be the most critical driver of in‐stream nutrient conditions during most seasons and under most flow conditions. Best management practices, such as vegetated buffer strips, should be most effective in controlling nutrient losses to southern Manitoba streams when situated within stream corridor, as opposed to upland areas, which appear to have minimal impact on in‐stream conditions. Copyright © 2013 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".