Blue Water Footprints of Ontario Dairy Farms
Bibliographic record
Abstract
The blue water footprint (WF) is an indicator of freshwater required to produce a given end product. Determining the blue WF for milk production, the seasonal water use and the impact of water conservation are important sustainability considerations for the dairy industry in Ontario (Canada). In this study, a water footprint network (WFN) method was used to calculate the seasonal blue WF’s from in-barn water use data and the fat–protein-corrected milk (FPCM) production. Various water conservation options were estimated using the AgriSuite software. Results showed that the total water use (L of water·cow−1·d−1) and the average blue WF (L of water·kg−1 of FPCM) were 246.3 ± 6.8 L·cow−1·d−1 and 7.4 ± 0.2 L·kg−1, respectively. The total water use and the blue WF could be reduced to 182.7 ± 5.1 L·cow−1·d−1 (25.8% reduction) and 5.8 ± 0.1 L·kg−1 (21.6% reduction), respectively, through adaptive water conservation measures as the reuse of the plate cooler and milk house water. For example, conservation practices could reduce the milk house wash water use from 74.3 ± 8.8 L·cow−1·d−1 to 16.6 ± 0.1 L·cow−1·d−1 (77.7% overall reduction).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".