MétaCan
Menu
Back to cohort
Record W1432749251

Drinking water quality and health-care utilization for gastrointestinal illness in greater Vancouver.

2000· article· en· W1432749251 on OpenAlexaffabout
Jeff Aramini, Margot McLean, Jean Wilson, John Holt, Ray Copes, Beth Allen, William Sears

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsHealth Canada
Fundersnot available
KeywordsWaterborne diseasesOutbreakWater qualityWater supplyEnvironmental healthWatershedWildlifeVulnerability (computing)BusinessContaminated waterGeographyEnvironmental planningEnvironmental protectionEnvironmental scienceEcologyMedicineEnvironmental engineeringBiology
DOInot available

Abstract

fetched live from OpenAlex

The risk of microbial disease associated with drinking water is presently a priority concern among North American water jurisdictions. Numerous past outbreaks, together with recent studies suggesting that drinking water may be a substantial contributor to endemic (non-outbreak related) gastroenteritis, demonstrate the vulnerability of many North American cities to waterborne diseases and have fuelled ongoing debates in Canada and the United States concerning the need for stricter water quality guidelines, changes in watershed management policies, and the need for additional water treatment. The Greater Vancouver Regional District (GVRD) water supply system serves approximately two million consumers from a system consisting of three unfiltered surface water supplies (Figure 1). Although GVRD policies reduce the potential for fecal contamination of the source water supplies by humans and domestic animals, the GVRD watersheds support many wildlife species that can potentially shed organisms pathogenic to humans. Because GVRD's water treatment strategy relies principally on watershed protection and chlorination*, and these two strategies together do not eliminate all risk of waterborne disease transmission, it is possible that some disease-causing organisms reach the consumer.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.074
GPT teacher head0.294
Teacher spread0.220 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations78
Published2000
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicFecal contamination and water qualityFrench-language works237,207