ADVANCES AND CHALLENGES WITH MICRO‐IRRIGATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT As global concerns surrounding water scarcity and food security escalate, there will be more demand for micro‐irrigation to meet growing food demands. Micro‐irrigation offers many advantages over conventional irrigation methods, including the ability to apply limited amounts of water directly to the crop root zone, incorporation of fertigation, reduced weed and pest infestation, and lower capital and operating costs. In recent decades, there has been considerable growth in the acreage under micro‐irrigation, mainly as a result of lower costs, improvements in filtration and emitter technology, and increased grower confidence in the technology. Research advances and technological improvements have made micro‐irrigation applicable to a more diverse set of applications, cropping systems, and water quality conditions. Cost and availability of water are also major drivers. Research in nano‐ and biofiltration techniques, soil moisture sensors, and precision irrigation shows great promise for the advancement of micro‐irrigation. Nevertheless, several technological challenges remain, especially for non‐row or non‐orchard crops, and in regions where water quality is severely impaired. Innovations in these areas are required, as well as a transfer of the technology to small farmers in water‐scarce regions who traditionally surface irrigate. Copyright © 2013 John Wiley & Sons, Ltd.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it