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Record W3003974559 · doi:10.15376/biores.15.1.787-813

Exploring the regional-scale potential of the use of wood products in non-residential buildings: A building permits-based quantitative approach

2019· article· en· W3003974559 on OpenAlexafffundabout
Sylvain Cordier, François Robichaud, Pierre Blanchet, Ben Amor

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsUniversité LavalUniversité de SherbrookeNatural Sciences and Engineering Research Council of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterial flow analysisEstimationEnvironmental scienceScale (ratio)Resource (disambiguation)Consumption (sociology)Life-cycle assessmentBuilding materialCivil engineeringComputer scienceEnvironmental resource managementArchitectural engineeringEngineeringProduction (economics)Waste managementGeography

Abstract

fetched live from OpenAlex

In the construction sector, wood products are gaining interest. Methods are necessary to quantify material use and evaluate their potential effects. When quantifying the building material consumption, many studies are limited to residential buildings due to the lack of data for non-residential buildings. This research aimed at investigating a methodology to account for non-residential building material consumption. A method to estimate the volume of wood products in the structures of the new non-residential buildings was presented. Then, projections of the estimation were suggested according to three scenarios (minimum, average, and maximum). Sensitivity analyses highlighted the parameters that present the greatest contribution to the scenarios. The relative importance of the estimation to the total harvesting of all wood markets was also assessed. Despite the high uncertainty in wood consumption for non-residential building structures, the estimation had a small weight on the total harvesting of the Quebec province. The results showed how and when the resource availability could be constrained depending on the assumptions. This method can serve for life cycle inventory for an environmental assessment or wood flow analysis, but more research on the material composition of the non-residential building archetypes is necessary.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.374

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 studies0.0000.001
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 head0.059
GPT teacher head0.246
Teacher spread0.187 · 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 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

Citations8
Published2019
Admission routes3
Has abstractyes

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