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Record W4237299589 · doi:10.1002/div.3744

Terasen Inc

2006· article· en· W4237299589 on OpenAlexaboutno aff

Bibliographic record

VenueMergent s Dividend Achievers · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Governance and Law
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline transportNatural gasFossil fuelPetroleumBusinessWastewaterSubsidiaryIrrigationEnvironmental scienceFinanceEngineeringEnvironmental engineeringWaste managementGeology

Abstract

fetched live from OpenAlex

Abstract Oil and Gas (MIC: 14.2 SIC: 1623 NAIC: 486910) Terasen is a provider of energy transportation and utility services. Through Terasen Gas, Co. distributed natural gas in British Columbia (B.C.) to more than 875,000 customers in more than 125 communities at Dec 31 2004. Through Terasen Pipelines, Co. provides petroleum transportation services from Edmonton, Alberta to Vancouver, B.C., Washington state, the U.S. Rocky Mountains region and the U.S. midwest. Terasen Water and Utility Services, subsidiaries of Co., are providers of water and wastewater treatment services as well as product sales related to the water, sewer and irrigation markets, operating over 90 systems in more than 50 communities throughout B.C., Alberta and Alaska.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.824
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.8240.708

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 head0.010
GPT teacher head0.182
Teacher spread0.172 · 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.

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
Published2006
Admission routes1
Has abstractyes

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