An analysis of the US decking materials market: perceptual mapping approach
Bibliographic record
Abstract
This paper analyzes the product positioning and market demand of preservative-treated, naturally decay-resistant, and composite decking materials in the United States using an innovative perceptual mapping technique. Market analysis using perceptual mapping is generally used for end-user market scenarios for strategic planning. This paper establishes that the perceptual mapping technique can also be a valuable tool for analyzing the US decking products market, which is predominantly a business-to-business (B2B) market. Using perceptual mapping of the US decking market, this paper proposes a method for incorporating market demand density on a perceptual space, incorporating a kernel regression based nonparametric demand distribution. Representation of products and demand distribution on the same perceptual space in the proposed mapping technique enables simultaneous visualization of product positioning and demand density distribution in the marketplace. The mapping results obtained in this paper will enable business decision makers in the US decking industry to strategically position their offerings based on existing product positioning and market demand distribution. The data used for the study were collected through a national survey of 368 professional deck builders and homebuilders.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".