Bibliographic record
Abstract
Assessing On-Farm Irrigation Water Use Efficiency in Southern Ontario In southern Ontario, irrigation is essential for high value horticultural crop production to overcome insufficient rainfall and achieve stabilized crop production. In a context where competition for limited water resources intensifies due to the expansion of the agricultural sector, increasing urban development and tourism, and potential climate change impacts, conserving water through efficient irrigation has become a key solution to address this growing challenge. The implementation of advanced soil water monitoring technologies and water budgeting for improved irrigation scheduling is explored to conserve water and thus cope with increasing competing demands for limited water supplies. Soil moisture was measured by gravimetric sampling in conjunction with several modern soil water sensors over the course of the 2007 growing season at 15 field sites located in southern Ontario where high value horticultural crop production is predominant. Quantities of irrigation water used were measured by flow meters that were installed at three of these sites. In addition, two grower surveys were administered: the first to collect information on current irrigation scheduling practices, and another to determine the appropriateness of the soil moisture monitoring sensors. On-farm irrigation performance was assessed by comparing calculated crop water requirements (using the water budget method) with growers' estimates of irrigation water use with soil moisture measurements taken during the growing season. In five out of six experimental zones, water was either excessively or insufficiently applied. In
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".