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Efficacy of Annual Bacteria Monitoring and Shock Chlorination in Wells Finished in a Floodplain Aquifer

2002· article· en· W2052794562 on OpenAlexaff
Jennifer Arletta Oliphant, M. Cathryn Ryan, Angus Chu, Timothy W. Lambert

Bibliographic record

VenueGroundwater Monitoring & Remediation · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsEnvironmental scienceAquiferWater wellGroundwaterContaminationWater qualityFloodplainHydrology (agriculture)EffluentFecal coliformShock (circulatory)Environmental engineeringSeptic shockColiform bacteriaBacteriaGeologyEcologyBiologyMedicine

Abstract

fetched live from OpenAlex

Abstract Public health authorities generally recommend annual water‐quality monitoring of rural water wells and shock chlorination if coliforms are detected. It is implicitly assumed that shock chlorination is effective in ridding most wells of bacteriological pathogens for months to years. Neither annual monitoring nor shock chlorination was effective in addressing coliform contamination of selected water wells in a small town developed on an alluvial aquifer where septic system effluents are impacting well water quality. Considerable temporal variation in total and fecal coliforms was observed in water wells monitored for a six‐month period. Individual wells intermittently met and exceeded the drinking water criteria, indicating annual sampling was insufficient. Shock chlorination of three contaminated wells and their associated distribution systems proved ineffective because colonies apparently originated from outside the wells and reappeared over relatively short time periods (ranging from less than one week up to 21 weeks). The relatively fast and similar rate of recovery of total heterotrophic bacteria suggested they are related to biofilm formation in the wells and not to ground water contamination.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.225
Teacher spread0.209 · 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

Citations5
Published2002
Admission routes1
Has abstractyes

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