Bibliographic record
Abstract
ABSTRACT With the increasing competition for fresh water, there is a growing reliance on the abstraction of groundwater for irrigated agriculture. Groundwater irrigation demand has been growing steadily over the past decades, for many reasons including the unreliability of the traditional large canal schemes, and the increasing need of farmers to manage their own irrigation applications. In addition, unpredictability in climate has forced some farmers, particularly in semi‐arid areas, to exploit groundwater, in order to combat drought. The increasing overexploitation of important aquifers around the world, as well as groundwater contamination must be of concern to water resource planners and managers. Groundwater is a finite resource, and little is being done to accurately map, assess, monitor and regulate groundwater development for agriculture. Unconstrained and unregulated groundwater development is already impacting negatively on agricultural growth in over 111 million ha of irrigated lands, and innumerable livelihoods that rely on groundwater. Any attenuation of growth will make it more difficult to feed 9 billion people by 2050. A more proactive and integrated approach to groundwater governance, management and protection, built upon sound technical, institutional, legal, socio‐economic and environmental principles is required. Copyright © 2012 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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".