Bibliographic record
Abstract
Introduction Floods are one of the world's most damaging and dangerous natural hazards. Jonkman (2005) estimated that, in the last decade of the twentieth century, fluvial and other drainage-related floods killed 100 000 and affected 1.4 billion people. Economic losses from floods are difficult to estimate precisely, but are large and increasing. Barredo (2009) estimated annual average losses for Europe to be $3.8 billion over the period 1970–2006 (at 2006 values), and UNESCO (2009) noted that losses from extreme events rose 10-fold between the 1950s and 1990s in real terms. Flood protection and flood management are therefore seen as important issues by society, and associated infrastructure and management systems represent large and ongoing investment (in the UK, annual expenditure on flood defence is of the order of £800 million). However, flood risk management is a complex and multi-faceted issue. Floods are part of the natural functioning of fluvial systems. This has important implications. Firstly, to understand flood response and to manage flood risk, there is a need for awareness of the nature of and controls on catchment response, including the effects of human interventions. Secondly, floods are essential for the maintenance of aquatic and riparian habitats, and hence a holistic assessment of flood management is necessary, including broader environmental considerations such as geomorphological and hydro-ecological aspects. There may therefore be tensions between the need to protect infrastructure and risk to life on the one hand and the need to maintain natural ecosystem function on the other.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".