Agricultural Water Use: A Methodology and Estimates for Ontario (1991, 1996 and 2001)
Bibliographic record
Abstract
In the absence of good quality data, effective and sustainable water management is a challenge. Agricultural water use presents a variety of special challenges to those striving to improve water use databases in Ontario. For example, agricultural water use is distributed among almost 60,000 farms in the province, with most farm operators being self-supplied. This paper presents a methodology for estimating agricultural water use using Statistics Canada Census of Agriculture data, applied to water use coefficients. Agricultural water use is estimated for five major sectors, for each of 1991, 1996 and 2001. Estimates for each year are then translated to a common spatial frame of reference (2001 Census Consolidated Subdivision boundaries). Spatial patterns of water use for 2001 are discussed and significant 1991-2001 changes are identified. If used appropriately, the estimates can offer insights into spatial and temporal patterns and trends that can inform broad-scale planning. Nevertheless, agencies with a stake in improving water use data in Ontario should continue to work together to ensure that measurement of actual water use occurs in as many sectors as possible.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".