External Drivers and Internal Control Factors that Determine the Vulnerability and Response Capacity to Drought of Cattle Producers in the Sierras Del Este Region of Uruguay
Bibliographic record
Abstract
Increased response and adaptation capacity are key elements for coping with climate threats. Cattle producers in the Sierras del Este region are one of several groups that are the most vulnerable to climate variability in Uruguay. Despite this commonality, it is a heterogeneous system, which suggests that strategies to respond to these events are divergent. The objective of this work is to identify and evaluate the vulnerability of cattle producers to drought and determine drought response strategies. A new approach is proposed and focuses on the identification of differential capacities to address the vulnerabilities. In addition, this approach seeks to define groups of similar producers of vulnerability since the design of public policies cannot be developed in isolation. For evaluation, we provided consultations with livestock producers and specialists from which we collected our data. Data was analysed using multivariate statistical analyses. Our results indicated that 69% of the system’s vulnerability variance can be explained by 4 components: the capacity for cattle management, the socio-economic capacity to handle drought, the capacity to generate alternatives to cattle feeding, and the commercial and financial flexibility of the producers. These findings also yielded response groups that, in turn, identified 7 producer groups with significant differences in the available and necessary capacities to respond to drought. This methodological strategy allowed the operationalization of the vulnerability and responsiveness concepts, and the identification of strategies for these events. Additionally, this strategy creates an understanding of the complexity of the system and the variables that contribute to it.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".