Bibliographic record
Abstract
Abstract At the global scale, the warming of the atmosphere will increase the capacity of the atmosphere to hold and accelerate the redistribution of water in the atmosphere. This suggests that flood‐generating processes linked to the atmosphere are likely to increase. However, the I ntergovernmental P anel on C limate C hange projections of future floods involve extremely complex issues that defy simple generalisations. Warming will alter other aspects of the water cycle increasing evaporation, changing precipitation patterns and intensity, and also affecting the processes involved in surface storage of water, including snowpack generation, snowmelt, river ice break‐up, and glacial melt. Many of these are active in flood generation, and changes may cause floods to decrease as well as increase. However, these processes take place not at the global scale but at relatively local scale, making generalisations about flooding in future climates difficult and uncertain. At the global scale, the role of land use is generally unresolved, but at a watershed scale, land‐use effects can be as important as changes in the meteorological processes. This review shows that while meteorologically driven flooding is expected to increase in a changed climate, making a precise pronouncement regarding all floods is unwise, as many types of floods will respond differently to changing climate and that because floods are watershed scale events, these local effects will remain important.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".