Building the resilience of Italy’s agricultural sector to drought
Bibliographic record
Abstract
Increasingly frequent and severe droughts are threatening Italy’s agricultural sector. With climate change forecast to accelerate these trends, the sector must build long-term resilience. This will require better planning and preparing for, absorbing the impact of, and recovering from droughts, as well as more successfully adapting and transforming in response to these events. Recent positive developments include improved data collection on water supplies and agricultural damage and loss from natural hazards to better inform water management and investment decisions; strengthened commitment to ex ante risk management frameworks; and more participatory approaches for water management. Nevertheless, the agricultural policy portfolio currently underemphasises investments in on-farm preparedness and adaptation, in favour of coping tools such as insurance. Further efforts to build agricultural resilience could benefit from a holistic, long-term sectoral risk management strategy; an evaluation of the trade-offs between spending on risk coping tools versus investments in natural hazard preparedness and measures to mitigate their impacts; and more explicit consideration of farmer demographics and capacities in policy design.
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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 | 0.002 | 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".