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Record W229635195

Whole Building Commissioning

2000· article· en· W229635195 on OpenAlexaboutno aff
Mark W. Lopez

Bibliographic record

VenueDefense Technical Information Center (DTIC) · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLife Cycle Costing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsProject commissioningYesterdayGovernment (linguistics)NavyProcess (computing)EngineeringPublishingOperations managementComputer sciencePolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

The primary goal of this paper is to familiarize the reader with the building commissioning concept. Building commissioning gets its name from the process in which the Navy commissions ships and submarines by ensuring the vessel performs properly as intended before they are put out to sea. It is also similar to processes used in the Pacific Northwest and Canada as well as in the construction of factories and industrial facilities. Designing, specifying, and ensuring performance are the keys to realizing the benefits of building commissioning. Building commissioning is a relatively new process just coming into focus in the 1990's. The first National Conference on Building Commissioning was held in Sacramento in 1993. It developed out of a 1992 Commissioning Roundtable meeting, sponsored by the Bonneville Power Administration, among Pacific Northwest and California utility representatives, federal and state government personnel, and energy professionals. Participants at the Roundtable agreed that the industry needed to establish a regular, national forum for the discussion of building commissioning. The driving force behind the insurgence of building commissioning has been the lack of "quality" from decades of low-bid, lowest-cost, corner cuffing. This current philosophy of shortsighted practices continues to produce mediocre, minimum code buildings that really ended up costing more to build and even more to operate. "Total" or "Whole" building commissioning is a proven process to replace these unreliable construction practices of yesterday by basically insisting on quality assurance

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.066
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0660.026

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 head0.016
GPT teacher head0.237
Teacher spread0.220 · 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
Published2000
Admission routes1
Has abstractyes

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