American beech in value-added hardwood products: Assessing consumer preferences
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".