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Record W4401904403 · doi:10.62592/mndh2138

Public Health Risk Assessment and Risk Management for Safe Drinking Water

2024· book· en· W4401904403 on OpenAlexfundno aff
Steve E. Hrudey

Bibliographic record

VenueThe Groundwater Project eBooks · 2024
Typebook
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsnot available
FundersUniversity of AlbertaUniversity of WaterlooUniversity of TorontoColorado School of Mines
KeywordsGroundwaterPublic healthEnvironmental planningWater supplyWater safetyRisk assessmentEnvironmental healthBusinessWater resource managementEnvironmental scienceEnvironmental engineeringEngineeringWater qualityMedicineComputer scienceComputer securityNursing

Abstract

fetched live from OpenAlex

Groundwater provides drinking water to more than 10 million Canadians, including more than 80% of rural populations. Ensuring that groundwater is safe for human consumption is a shared responsibility of any scientist or professional engaged in a domestic or municipal groundwater supply. All such individuals should understand the basis for judging the safety of drinking water, particularly, the practical limitations of the techniques for making those judgements. This is true for public health risk assessment of drinking water, just as groundwater hydrogeologists need to understand the uncertainties and limitations of the methods they must rely on for understanding and interpreting groundwater resources. This book seeks to provide an insight into the processes used for judging the health risks associated with contamination of drinking water so that groundwater specialists can engage with public health officials in meaningful discussion and decision-making about the best means for effectively managing health risks associated with drinking water.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0210.008

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.

Opus teacher head0.042
GPT teacher head0.276
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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