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Record W2766671803 · doi:10.1139/cjce-2017-0241

Environmental risk factors for bacteriological contamination in rural drinking water wells in Samson Cree Nation

2017· article· en· W2766671803 on OpenAlexaffvenueabout
Fraser Mah, Travis Hnidan, Evan Davies, Ania C. Ulrich

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsContaminationEnvironmental scienceWater wellRemedial actionWater contaminationEnvironmental engineeringPrecipitationGroundwaterWater sourcePeriod (music)Water resource managementEnvironmental remediationEnvironmental protectionGeographyEngineeringEcologyBiology

Abstract

fetched live from OpenAlex

Bacteriological contamination of drinking water wells poses a challenge to many rural areas of First Nations communities in Alberta that rely on wells as the primary drinking water source for large proportions of their populations. Here we reviewed available historical data for the Samson Cree Nation near Maskwacis, Alberta (formerly Hobbema), to identify linkages between various environmental and historical factors and the risk of contamination by Escherichia coli and total coliform bacteria. Increased bacterial counts were found to be associated with a peak in total precipitation and surface water flow following a two to four month lag period. Wells installed by contractors operating at an earlier period in time were also found to be at greater risk than more recent installations. These findings can be used to better characterize the risk of contamination, which will assist in tailoring remedial actions to address chronic or recurring bacteriological presence in wells.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.623

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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 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

Citations12
Published2017
Admission routes3
Has abstractyes

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