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Record W2531220712

Exploring Appropriate Business Models for Establishment of Water Quality Monitoring Service In Newfoundland and Labrador

2015· dissertation· en· W2531220712 on OpenAlexaboutno aff
Atanu Sarkar, Thomas Cooper, Kalen K. Thomson, Md. Arifur Rahman

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2015
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsMandateWater qualityWater supplyGovernment (linguistics)Environmental planningQuality (philosophy)BusinessEnvironmental resource managementEnvironmental scienceWater resource managementEnvironmental engineering
DOInot available

Abstract

fetched live from OpenAlex

The government of Newfoundland and Labrador is committed to providing the public with clean and safe drinking water. In order to achieve this goal a number of actions have been initiated. For example, the multi-barrier approach includes source protection, water treatment, water system operation and maintenance, water quality monitoring and reporting, regulatory inspection and \nmitigation planning, and operator education and training (DOEC, 2015). Public water supply systems treat water to ensure free from any microbiological contamination. The Department of Environment and Conservation (DOEC) also measure several non-microbial parameters of the Guidelines for Canadian Drinking Water Quality (GCDWQ) as the indicators of water quality. However, private water sources are outside this mandate and lack mandatory treatment and monitoring guidelines.

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)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.778

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0070.003
Scholarly communication0.0190.008
Open science0.0030.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.101
GPT teacher head0.312
Teacher spread0.211 · 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 designQualitative
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

Citations0
Published2015
Admission routes1
Has abstractyes

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