A marketing study of consumer acceptance & perceived greenness of wood concrete products
Bibliographic record
Abstract
Market research was conducted to determine consumer response and perceptions regarding a novel building material.Mountain Pine Beetle Wood Concrete Products (MPBWCP) are wood based products made from a concrete-like compound, with wood fibre from dead pine trees used in place of aggregate.The properties of MPBWCP make it ideal for a wide variety of applications, such as countertops, tiles, garden blocks and decorative uses.This research entailed a survey completed by 210 respondents in different consumer groups: industrial consumers, professional consumers, home consumers, and environmental organization supporters.The primary fmding of the research was that, on average, consumers are quite interested in learning more about and potentially using MPBWCP as a building material.They also perceive it to be a green product.Significant differences were noted between the groups of survey respondents for several questions, especially when considered on an occupational level.These fmdings will serve as a guide for those involved in the eventual launch of MPBWCP as a commercially available building material, as well as for others attempting to market a wood based product.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".