Character-marked furniture made from red alder harvested in southeast Alaska: product perspectives from consumers and retailers
Bibliographic record
Abstract
In recent decades, red alder ( Alnus rubra Bong.) has become an important Pacific Northwest hardwood in appearance-grade lumber markets, such as exports, furniture, and cabinets. However, red alder generally is a short-lived pioneer species, and small logs can result in proportionally large volumes of lower grade lumber containing numerous visual defects, such as knots, often referred to as character marks. Given that markets for character-marked wood could provide an income stream for management of red alder, it becomes important to understand consumer and retailer response to character-marked red alder products. In the current study, we used a policy capturing approach (the lens model) to assess the cues used by furniture consumers and retailers to evaluate several furniture pieces constructed from character-marked red alder lumber. The cues used by consumers and retailers to form willingness-to-pay judgments were found to be different. Character marks, design, and naturalness were important to consumers. None of the investigated cues were significant to retailers, suggesting they were using an entirely different model. Such divergence creates challenges in the forestry supply chain for development of new forest products.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".