Irrigation in the context of today's global food crisis
Bibliographic record
Abstract
Abstract During 2008 the world witnessed a global food crisis which caused social unrest in many countries and drove 75 million more people into poverty. The crisis resulted from sharply higher oil prices, increased bio‐fuel production, dwindling grain stocks, market speculation, changing food consumption patterns in emerging economies, and changes in world trade agreements, among other factors. Although the rise in food prices was sudden, the fragility of global food security had been developing for years. During the 1960s and 1970s food production kept pace with demand as more cropland was irrigated and yields of irrigated crops increased dramatically. Irrigation played a critical role in combating hunger, poverty and death due to malnutrition. However, the environmental and social consequences of large irrigation schemes came into question, and investments in irrigation subsequently diminished. Today's food crisis is compounded by a rapidly growing world population, the conversion of food producing lands to bio‐fuel production, diminishing available freshwater supplies, competition for water by other sectors, climate change impacts, and the reduction in arable lands due to urbanization. It is critical that investments focus on increasing agricultural production through improved management of land and water resources, and the involvement of all stakeholders. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".