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Examining Trends in the Nonresidential Building Construction Producer Price Indexes (PPIs)

2014· article· en· W21680053 on OpenAlexaboutno aff
Justin M Harper

Bibliographic record

VenueJournal of Plant Physiology · 2014
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsnot available
Fundersnot available
KeywordsIndex (typography)Inflation (cosmology)Gross domestic productPrice indexQuarter (Canadian coin)Investment (military)EconomicsValue (mathematics)Construction industryBuilding constructionPrivate sectorGross fixed capital formationAgricultural economicsBusinessEngineeringMacroeconomicsEconomic growthStatisticsMathematicsGeographyComputer science

Abstract

fetched live from OpenAlex

In 2004, the Bureau of Labor Statistics (BLS) unveiled the Producer Price Index (PPI) nonresidential building construction initiative with the publication of an index for new warehouse building construction. PPI has since added nonresidential building construction indexes for schools, offices, industrial buildings, and health care buildings. This construction sector initiative is noteworthy as it expanded coverage into an important sector of the U.S. economy not previously measured by the PPI, and allowed the examination of different drivers of building construction inflation. According to Bureau of Economic Analysis (BEA) data, in the first quarter of 2005, the value of private fixed investment in structures totaled $1.137 trillion, representing about 8.9 percent of total gross domestic product (GDP). Of private fixed investment in structures, nonresidential structures alone represented $330.8 billion, or about 2.6 percent of total GDP. By the fourth quarter of 2013, nonresidential structures investments grew to $473.4 billion, or 2.8 percent of total GDP.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.280
Teacher spread0.254 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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