Institutional Management and Planning for Droughts: A Comparison of Ireland and Ontario, Canada
Bibliographic record
Abstract
ABSTRACT: Severe drought conditions in 2018 prompted concerted efforts by Irish authorities to establish a formal planning process for drought risks as part of the wider national water management strategy. More than two decades had passed since Ireland had experienced a socioeconomically significant drought, but recently reconstructed long-term data have shown that drought is a much more frequent hazard here than previously thought. With climate change impacts likely to affect the temporal and spatial distribution of precipitation in coming decades, there is an ongoing need for further planning and preparation to reduce the vulnerability of the Irish water system to droughts. In this article we report results of a systematic comparison of Irish drought management plans and policies with those in southwestern Ontario, Canada, a region that shares many similar drought risk factors and management challenges but has longer established institutional practices for managing droughts. Key recommendations for Irish water managers emerging from this project include fostering a culture of water conservation among the Irish public; using catchments as the spatial unit for drought monitoring and management decisions; creation of standing drought management teams that involve and broaden key stakeholders and user groups; and further refining data collection to support planning for future challenges associated with climate change. Pursuing future opportunities for peer-to-peer learning between Irish water managers and their counterparts in other jurisdictions is a wider opportunity for developing best practices for drought management in the Irish context.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".