Comparison Of Subsurface And Surface Drip Irrigation Systems Using Responsive Sensor Control In A Controlled Indoor Environment In Ontario
Bibliographic record
Abstract
Throughout history, humans have depended largely on agricultural products for basic survival. As technology continues to develop, new ways of more efficiently cultivating these crops have emerged. People have tried various different techniques: using better soil, changing irrigation schedules and methods, and seed improvements to list a few, in hopes of improving yield. This study explores the effectiveness of subsurface irrigation compared to traditional surface drip irrigation in water conservation whilst maintaining healthy crop growth. Using baby heirloom lettuce as the test plant, both irrigation systems were designed and monitored under controlled indoor conditions, with soil moisture sensors changing irrigation schedule based on readings. By doing so, the study aims at determining how subsurface irrigation and responsive sensor control can improve irrigation efficiency. This offers insights into sustainable, data-driven farming practices, allowing plant growers around the world to have a more optimized and efficient system. The results showed that subsurface irrigation is better for plant growth compared to above surface drip, but uses slightly more water.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".