Do Water Contamination Reports Influence Water Use Practices on Feedlot Farms and Rural Households in Southern Alberta?
Bibliographic record
Abstract
This article explores the extent to which water contamination reports influence water use practices of feedlot operators and their households in southern Alberta. An in–person survey was conducted with 33 feedlot farm families living in the Lethbridge Northern Irrigation District. The analyses reveal that there are variations in operators’ knowledge of local water contamination reports and the ways in which these reports influence water use practices. For example, while 88% of participants were aware of reports that the South Saskatchewan River Basin has a very high level of pesticide residues, only 24% said that this has always influenced the way they use their water for domestic use. While this study provides insight into understanding the relationship between water contamination reports and water use practices of feedlot farm families, it also serves as a starting point for a more extensive socioeconomic and health survey focused on this population.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".