Sixth International Conference on Flood Management (<scp>ICFM6</scp>): Floods in a changing environment, part 1
Bibliographic record
Abstract
The ICFM6 (Sao Paulo 2014) conference devoted considerable attention to urban flooding (the host city Sao Paulo being an excellent example). 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. The current challenges to managing urban flooding and examination of potential solutions are presented in this first part of the special issue. The risk of flooding is defined as a function of both the probability of a flood happening and its impact. In urban areas, the impact of flooding is very high because the areas affected are densely populated and contain vital infrastructure. Continuing development in flood-prone urban areas increases the risk. In municipalities across the world, several factors have resulted in increased flood damage and there is an indication that the situation will likely get worse in the near future. The sources of concern include: (i) Social and demographic change. Global population has been increasing and there has generally been more exposure of people to floods. There has also been a growing inequality in the difference between poor and wealthy members of society and the poor are more vulnerable. (ii) Changes to built environment. The density of buildings has been growing and most of the water infrastructure is old and its condition is unknown. As new developments cover previously permeable ground, the amount of rainwater running off the surface into drains and sewers increases dramatically. The proportion of impermeable ground in existing developments is increasing too. (iii) Changes to the physical environment, specifically climate change. (iv) Changes in flood management policy. If the general message can be presented, the publication included in this special issue point out that multiple sources of flood risk must be considered together. The separate sources of coastal, river, rainfall, and groundwater flooding often occur together and the combined impact can be greater than the sum of each alone. Each source of flooding has different frequencies and so the consideration of risk as ‘Probability × Consequence’ is useful. However, this must include the need to focus on low-probability, high-consequence extreme events that may destroy the ability of a particular society to recover, i.e. an existential risk. Flood response features often have benefits beyond flood risk, i.e. environmental and societal benefits. It needs to be ensured that these are taken into account in any cost–benefit analysis. The wider economic impacts of flooding, sometimes beyond the geographical area where they occur, should be taken into account in any cost–benefit analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.093 | 0.039 |
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".