Rural water use and conservation in southwestern Ontario
Bibliographic record
Abstract
While Ontario appears to have an abundance of freshwater, shortages occur frequently. Little is known about the nature and extent of use of various water conservation practices in rural areas. Mail-back questionnaire responses from 291 agricultural and rural non farm property owners revealed that most practice some form of water conservation. Analysis indicated that household water saving was more commonly practiced indoors than outdoors. Non farm respondents were more likely to reduce water use through daily behavioral modifications such as shortening shower times, than to install water saving devices in the home. Agricultural respondents were more likely to reduce water waste in the home, primarily by keeping household appliances, pipes, and taps working efficiently. Livestock operators favored water equipment maintenance over all other livestock water saving measures. Irrigators were more likely to adopt a series of conservation measures, most commonly scheduling irrigation and reducing water need of agricultural crops. Statistical analysis revealed that adoption of water conservation measures in the home was best explained by program awareness and participation, level of formal education, and anticipation of future water shortages. Higher levels of livestock water conservation were associated with several factors, including greater farm gross sales and agriculture as the primary income source. Awareness programs aimed at household water conservation may prove effective in rural households, although somewhat less effective in agriculture. Financial incentives to aid in the installation of water saving equipment, along with awareness programs, may be necessary to improve water conservation within the agricultural sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".