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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".