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Stae of the Industry Report 2005: A Guide for Good Health

2005· article· en· W1533654442 on OpenAlexaboutno aff
Jon Runge, J. Y. Mann

Bibliographic record

VenueAmerican Water Works Association · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessBusinessMarketingWater industryState (computer science)Critical success factorHealthcare industryHealth carePublic relationsWater supplyEngineeringEconomicsManagementEconomic growthPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The results are in from the second annual State of the Industry survey – a comprehensive evaluation of the water industry's overall health. The 2005 State of the Industry report presents key findings from the survey, which included the responses from more than 1,700 utility personnel, service providers, and other individuals. Like the 2004 report, this year's report helps recognize and track significant trends, identify critical and emerging issues – especially those being inadequately addressed – assess water professionals' perception of the relative health of the industry, and provide data that can help the industry prioritize programs and services. In 2005, survey respondents considered the most critical issues facing the overall water industry to be regulatory factors, business factors, source water supply, security, and water storage/ infrastructure, and they identified industry leadership as a new issue. In addition, the 2005 report includes a new look at issues broken down by key US regions and from a Canadian perspective. This year's report also identifies barriers to preparedness and provides a look at utility capital spending; it offers insight into areas of strength and weakness in today's water industry and potential future challenges. The information contained in this article can be used to give direction to the industry's current and future leaders and guide them in directing resources for the healthiest tomorrow.

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 score0.148
Threshold uncertainty score0.324

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 communication0.0000.000
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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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

Citations21
Published2005
Admission routes1
Has abstractyes

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