Quantity and Quality of Runoff from a Beef Cattle Feedlot in Southern Alberta
Bibliographic record
Abstract
Southern Alberta, which has a cold climate dominated by strong chinook winds, has the highest density of feedlot cattle in Canada. However, the quantity and quality of runoff from beef cattle (Bos taurus) feedlots in this unique region has not been investigated. Our objectives were to compare runoff quantity (1998-2002) with catch-basin design criteria; determine concentrations of selected inorganic chemical parameters (1998-2000) in runoff in relation to water quality guidelines and the potential implications of irrigating adjacent crop-land; and determine if total heterotrophs, total coliforms, and Escherichia coli (1998-2000) persisted in the catch-basin water and soil. Runoff (< 0.1 to 42.5 mm) for a 24-h duration that included maximum peak discharge was less than the recommended design criteria of 90 mm based on runoff from 24 h of rainfall with a 30-yr return period. We found that curve numbers between 52 and 96 (mode of 90) were required to match the USDA Natural Resources Conservation Service predicted runoff and actual runoff volumes. Total P posed the greatest threat to water quality guidelines, and K posed the greatest threat for exceeding crop fertilizer requirements if catch-basin effluent was used as irrigation water. Water in the catch basin had continually high populations of E. coli throughout the study, with values ranging between log10 2 and log10 8 100 mL(-1). In contrast, soil in the catch basin generally had low populations of E. coli that were < log10 2 g(-1) wet wt., but at times higher populations between log10 2 and log10 6 g(-1) wet wt. were also found.
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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 | 0.000 | 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".