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Shedding Light on a Dark Situation

2003· article· en· W2808048816 on OpenAlexaboutno aff
Carol Carpenter

Bibliographic record

VenueOpflow · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransportation Systems and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsBlackoutGuard (computer science)Water tanksNational guardEnvironmental sciencePower (physics)EngineeringEnvironmental engineeringComputer scienceElectric power systemPolitical sciencePhysics

Abstract

fetched live from OpenAlex

This article describes the experience that the Cleveland Division of Water (CWD) had with the massive electrical power outage that occurred on August 14, 2003. Not only Cleveland, but all of northern Ohio, several other northeastern states, and parts of Canada were affected. CWD lost 50% of its total 135 mil gal of water stored in an underground reservoir, 777 mil gal of water stored in 13 storage tanks (four ground‐level steel tanks and nine elevated towers), and much of the water stored in four other ground‐level and elevated tanks that hold 33.5 mil gal each. The article tells how staff coped with the massive problem, how extra staff was deployed including the National Guard, and how CWD communicated with customers during the power blackout.

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)0.005
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0360.030
Scholarly communication0.0150.018
Open science0.0020.014
Research integrity0.0060.018
Insufficient payload (model declined to judge)0.0130.002

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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
GenreOther

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
Published2003
Admission routes1
Has abstractyes

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