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Rapid microbial methods can improve public health protection

2010· article· en· W2271319420 on OpenAlexaboutno aff
Martin J. Allen, Pierre Payment, Jennifer L. Clancy

Bibliographic record

VenueAmerican Water Works Association · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsnot available
FundersDrinking Water InspectorateU.S. Environmental Protection Agency
KeywordsCertificationPublic healthCompliance (psychology)BusinessChromogenicTurnaround timeEnvironmental healthRisk analysis (engineering)MedicineOperations managementEngineeringNursingPolitical scienceChemistry

Abstract

fetched live from OpenAlex

Regulatory, political, and institutional barriers have prevented broader use of rapid, simple, and inexpensive microbiological tests for Escherichia coli. By permitting greater use of chromogenic microbiological methods for compliance requirements by trained and certified operators of smaller public water systems, the cost of compliance should remain the same or decrease than when using distant laboratories. In fact, the lower cost of such methods would allow more frequent testing. New microbial methods allow greater public health protection because they are more sensitive to smaller amounts of contaminants in addition to allowing faster turnaround times, which would allow faster notification to the public. This article describes the current status of chromogenic enzyme tests; the experiences of Alaska and some Canadian provinces/territories in their onsite use, especially for facilities serving remote areas; and, the barriers that must be addressed before they can be broadly adopted for treatment performance or compliance monitoring.

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)0.014
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0250.006

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 head0.019
GPT teacher head0.284
Teacher spread0.265 · 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
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

Citations7
Published2010
Admission routes1
Has abstractyes

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