MétaCan
Menu
Back to cohort

Water Industry Well‐represented in Magazine's Selection of 2007 Trendsetters

2008· article· en· W4251154838 on OpenAlexaboutno aff

Bibliographic record

VenueAmerican Water Works Association · 2008
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsManagementWindsorGovernment (linguistics)George (robot)Water developmentPublic administrationEngineeringLibrary sciencePolitical scienceWater resourcesHistoryArt history

Abstract

fetched live from OpenAlex

Public Works magazine has named its 2007 Trendsetters in it's November 2007 issue. A member of AWWA's staff, a number of AWWA members, and several other organizations and people related to the water industry are on the list of the 50 most influential people, places, or events of the year. This article includes the following award winners: Kevin Morley, an environmental policy analyst in AWWA's government affairs office in Washington, D.C.; Daphne Utilities, a public water, sewer, and natural gas utility in Daphne, Alabama; Rafael Frias, a project engineer with Black & Veatch; Eric Hoek of the University of California, Los Angeles; Alan Hollenbeck of Winfield, Illinois; David Lewis of Grand Falls‐Windsor, Newfoundland; Paul Michael Schultz, chief of construction management with the Bureau of Water and Wastewater of the Baltimore (Maryland) Department of Public Works; Susan Seacrest, founder of the Groundwater Foundation in Nebraska; Michael Stuver, Roger Kjelgren, and Kelly Kopp were part of a joint effort by Utah State University and the U.S. Bureau of Reclamation; Nikolay Voutchkov, corporate technical director at Poseidon Resources; US Rep. John Dingell, a 27‐term Democrat representing Detroit, Michigan, who chairs the House Energy and Commerce Committee; The Georgia Tech Research Institute's Environmental Radiation Center; and, The Chicago (Illinois) District of the U.S. Army Corps of Engineers.

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 score0.064
Threshold uncertainty score0.513

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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Water Works AssociationSame topicUnderground infrastructure and sustainabilityFrench-language works237,207