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Estimating wooden prefabricated building export potential from the Province of Quebec to the northeastern United States

2021· article· en· W3199593318 on OpenAlexafffundabout
Allan Cid, Pierre Blanchet, François Robichaud, Nsimba Kinuani

Bibliographic record

VenueBioResources · 2021
Typearticle
Languageen
FieldEngineering
TopicRecycling and utilization of industrial and municipal waste in materials production
Canadian institutionsFPInnovationsUniversité LavalNatural Sciences and Engineering Research Council of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAgricultural economicsProduction (economics)CensusSupply and demandBusinessGeographyEconomicsPopulationDemography

Abstract

fetched live from OpenAlex

The import activity of wooden prefabricated buildings in the Northeastern US region was over CAD 41.8 million during 2019, according to the US Census Bureau. This amount was growing at a 12.5% annual rate on average since 2017. There is evidence of a continued shortfall in supply for the construction market to be overcome in the region. The objective of this study was to estimate the export potential of wooden prefabricated buildings from the Province of Quebec to the Northeastern US region for the next decade in relation to the export activity and production capacity of the industry. The value of annual production of wooden prefabricated buildings in Quebec was up to CAD 578 million in 2019, according to iCRIQ. Export activities from Quebec are mainly directed to the Northeastern US, and were of CAD 18.8 million in 2019, or 81% of Quebec’s wooden prefabricated building exports. Results suggest that potential for wooden prefabricated building exports from the Province of Quebec to the US Northeastern region is important in terms of market share. The study also suggests that by drastically increasing the production capacity of the industry there is no chance that supply will overcome demand.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.547

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 head0.017
GPT teacher head0.230
Teacher spread0.213 · 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 designSimulation or modeling
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

Citations3
Published2021
Admission routes3
Has abstractyes

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