Adopting a Preparatory Strategy to Respond to Water Security Issues Arising from Geo-Hazards
Bibliographic record
Abstract
‘Geo-hazards’ is a collective term to describe hazards causing huge problems with human settlements, where the hazards are many and varied, including earthquakes, floods, windstorms, and drought, all of which are intensifying over time in large part due to climate change and population growth. In particular, issues of availability of ‘safe’ water are major disruptive elements frequently causing widespread incidence of diarrheal diseases both during and post, geo-hazard events. In response, arguments are described which demonstrate ceramic water filters (CWFs) have credible potential to effectively remove E.-coli (and, by similar attribute characterization), are effective in the removal of cholera. Field experience in terms of removal have been demonstrated as 94.7% removal of E-coli and all users in some applications have expressed interest in continuing use of ceramic filters beyond the trial period. Arguments are put forth, for CWFs as a Point-of-Use (POU) technology by which they can be stored and rapidly disseminated given occurrence of geo-hazards, thereby providing the opportunity to respond quickly. CWFs can be effectively stored without deterioration, are inexpensive, and easy to train recipients for their post-geo-hazard occurrence.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".