Bibliographic record
Abstract
INTRODUCTION: Flooding accounts for about 40 per cent of all natural disasters that occur worldwide. In 2002-2003 many counties in England experienced severe floods. Floods are particularly important in public health terms as they may have multiple environmental consequences. METHODS: Details of floods reported to Chemical Hazards and Poisons Division, London [CHaPD(L)] were analysed and a literature review was undertaken to identify published reports of flood-related chemical incidents that have had an impact on public health. RESULTS: Epidemiological evidence shows that chemical material may contaminate homes and that in some cases flooding may lead to mobilization of dangerous chemicals from storage or remobilization of chemicals already in the environment, e.g. pesticides. Hazards may be greater when industrial or agricultural land adjoining residential land is affected. Less evidence exists to support the hypothesis that flooding that causes chemical contamination has a clear causal effect on the pattern of morbidity and mortality following these flooding events. CONCLUSION: In the light of this evidence, a checklist/pro forma for public health response to and investigation of flooding events that may result in chemical contamination was needed. This is available from CHaPD(L).
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".