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American beech in value-added hardwood products: Assessing consumer preferences

2018· article· en· W2889697481 on OpenAlexaffabout
Anne Bernard, Nancy Gélinas, Emmanuel Duchâteau, Claude Durocher, Alexis Achim

Bibliographic record

VenueBioResources · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBeechHardwoodBark (sound)Aesthetic valueYellow birchResource (disambiguation)ThinningForestryGeographyAgroforestryBusinessPulp and paper industryMarketingEnvironmental scienceEcologyEngineeringBiologyComputer scienceArt

Abstract

fetched live from OpenAlex

The depleted state of the northern hardwood forests of Quebec, Canada has forced the hardwood flooring industry to adapt its production. American beech (Fagus grandifolia), a traditionally less desired species, is now increasingly being included in wood supplies to sawmills in western Quebec, where forest managers hope this resource can be valued before the onset of significant mortality and wood degradation from the beech bark disease. This study aimed: 1) to assess the preferences of consumers towards American beech flooring products compared to well known species traditionally used in this market; and 2) to compare results obtained in face-to-face surveys with web-based surveys of consumer preferences. Results from both survey types revealed that the finishing colour was the most important factor affecting the decision of respondents, followed by species and price. American beech ranked third in species preferences, just above birch. Divulgating species names only affected (positively) the perception of respondents towards oak. It was concluded that American beech could be included in the current wood flooring market, probably among cheaper options such as birch. The similarity of results from face-to-face and online surveys suggests that general trends in consumer preferences could be rapidly and cheaply assessed using the latter option.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2018
Admission routes2
Has abstractyes

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