Smart Monitoring of Irrigation Systems for Reduction of Water Consumption
Bibliographic record
Abstract
Water is essential to life both as drinking water and for growing crops. It is estimated that over 700 million people do not have access to clean safe drinking water worldwide [1]. It is critical to responsibly use water so that it remains a sustainable resource. One way to do this is to focus on optimising water usage when irrigating crops. Currently, there are three categories of irrigation methods: sprinkler, micro and surface irrigation. Sprinkler systems distribute water at a high-velocity and high-volume spray. Micro irrigation systems deliver water close to the crop either at the surface or below the soil. Surface irrigation provides water to the crops by flowing water over land [2]. These methods of watering are inefficient and wastes water due to their lack of feedback and control of soil saturation. To achieve automation of watering the project involves designing and building water soil sensors to monitor moisture levels. Each sensor would be connected to a network of sensors that are placed just below the soil (roughly 5 inches under the soil) and would accurately detect specific areas of the crops that need watering. By using technology paired with intelligent software low grade sensors can be used to monitor each watering zone determining whether that zone needs watering or not. Discipline: Computer Sciences Faculty Mentor: Dr. Shelley Lorimer
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".