Drought risk management in Mexico: progress and challenges
Bibliographic record
Abstract
Drought is one of the most complex natural phenomena, which affects the most people in the world.In Mexico, drought has been a recurrent and persistent problem throughout its history.In recent years, drought has affected large agricultural areas and rural communities, leading to severe imbalances in the regional and national economies, as occurred during the 2011-2012 drought, the most severe of the last 70 years.Therefore, in this paper an analysis of the measures that have recently been implemented to cope with drought in Mexico, which highlights the beginning of the transition from a reactive approach based on the crisis management towards a proactive approach aimed to risk management, with the implementation of the National Program Against Drought (PRONACOSE, for its acronym in Spanish) launched in 2013 is presented.So, in this paper, the components of this program are presented, along with a brief description of the Programs of Preventive and Mitigation Drought Measures (PMPMS, for its acronym in Spanish), which have been formulated as an integral part of PRONACOSE for each of the 26 basin councils in the country.Similarly, some of the main future challenges in drought management and research needs identified during the formulation of the PMPMS are exposed.We concluded that there is no way to avoid a drought but there are ways to mitigate its impacts and reduce losses of those affected by the phenomenon.Drought risk can't be completely eliminated, but preventive actions implemented in the future will be useful to mitigate its effects.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".