Bibliographic record
Abstract
Introduction Of the various types of natural hazards to which human settlements are exposed, flooding affects the greatest number of people worldwide (Jonkman 2005). The Millennium Ecosystem Assessment suggests as many as 2 billion people worldwide may live in areas exposed to flooding risk (Millennium Ecosystem Assessment 2005). The vast majority of the world’s nations – an estimated 85 per cent – have experienced significant flood events in recent decades (Bakker 2009). Like droughts and extreme storms, floods are inherent, regularly occurring outcomes of climatic processes. In some regions, flooding is an annual risk because of high seasonal variability in precipitation. In others, floods occur irregularly. The size and scale of the human impacts of extreme floods can be tremendous. There are the immediate risks of death from drowning or injury from flowing debris, outbreaks of waterborne and insect-borne diseases generated by standing waters, and the longer-term mental health effects of experiencing such a crisis (Ohl 2000, Ahern et al. 2005). Housing, businesses, and critical infrastructure may be destroyed or damaged, creating enormous financial costs and requiring much time and effort to repair and replace. Yet, while floodplains are inherently hazardous, they also tend to make especially suitable locations for human settlements, providing easy access to fertile soils, potable water, hydraulic flow for irrigation and powering turbines, and the potential to transport goods and people by boat. Researchers estimate floodplains provide more than a quarter of all ecosystem services consumed by humans, despite representing a small fraction of the Earth’s surface (Tockner and Stanford 2002). It is therefore not surprising that human population numbers continue to rise in many of the world’s most flood-prone river valleys and deltas despite the risks.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".