Sixth International Conference on Flood Management (ICFM6): Floods in a changing environment, part 2
Bibliographic record
Abstract
During the period between ICFM5 (Tokyo 2011) and ICFM6 (Sao Paulo 2014), floods are continuing to kill people, misplace people, and incur damage. Over 300 major events resulted in more than 14 000 fatalities, over 15 million misplaced people, and large material damage. This special issue offers a selected number of papers out of 314 presented at the conference (110 oral and 204 poster presentations) that attracted over 230 participants from 32 countries. A variety of topics included in this issue range from flood risk management policy to prediction of inputs necessary for flood emergency management, flood risk assessment, and flood damage mitigation. The message generated from the conference and selected papers provided in the special issue is that flood risk cannot be eliminated. However, a coordinated, system-wide response can dramatically reduce the impact of flooding. A flood resilience approach of ‘living with floods’ is becoming essential. Whilst continuing to allow for improved resistance to flood threats this also entails planning and preparation for quicker and more complete recovery from any floods suffered. The resilience approach of accepting floods and learning to live with the risk includes a requirement for sustainable features. In the context of future uncertainty over the rate of climate and socio-economic developments, this approach also means that an adaptive approach with flexible measures is advocated. More research and case studies are required to give decision-makers the confidence to apply this concept routinely. Preparation by individuals, communities, businesses, local authorities, nations, and regions is essential in reducing impacts. This covers information provision (early warning systems, risk mapping, and vulnerability indices), large-scale defences (embankments, retention structures, etc.), property-level defences, land-use planning, insurance, and emergency response. Understanding the preparation by individuals requires research on attitudes to risk and the balance people expect between their own responsibility and that of their government. Thus, flood risk management requires a range of disciplines including engineering, environmental science, information science, psychology, social science, economics, law, governance, and cultural studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.088 | 0.033 |
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".