Fibre supply issues of the British Columbia log home manufacturing industry
Bibliographic record
Abstract
British Columbia's log home manufacturing industry's fibre supply problems have been documented since 1992. To better understand the issues, an industry wide mail survey of 171 companies was conducted to assess production, sales, markets employment, fibre sources, fibre consumption, fibre costs and alternate fibre usage. The responses provided information on the current status of the industry, its fibre supply characteristics and use of alternative fibre supplies, such as insect killed, fire killed and second growth timber. A sample size of 42 resulted in a 24.6% response rate achieved in the mail survey. Over 90% of respondents reported using hand crafted production methods. The most common product category manufactured was the custom log home "not to lock up" with an average unit price of $71,100, which generated $905.36 in sales per cubic metre of fibre utilized. On average, each respondent consumed 1723 cubic metres of fibre, employed 12.5 full time equivalent personnel and produced 14.6 units per year to generate gross annual sales of $1.1 million. The major markets for BC's log home industry were the United States (46%), British Columbia (31%) and Asia (13%). Of the 55,119 cubic metres of fibre recorded as purchased by respondents, 69% was from the Vancouver and Kamloops forest regions. This fibre was primarily obtained from logging company dryland sortyards, with Douglas fir (Pseudotsuga menziesii [Mirb.] Franco) the preferred species. The weighted average delivered price of all fibre purchased was $153.67 per cubic metre. In addition to old growth timber, insect killed and second growth timber were used as alternative fibre sources with 58.5% of respondents reporting usage of these materials. Just over 26,300 cubic metres of this alternative fibre was purchased by respondents with a weighted average delivered price of $127.60 per cubic metre. The respondents' opinions indicate inconsistent fibre supply and high fibre costs had the greatest impact on the industry's performance. To resolve fibre supply issues, the log home manufacturing industry strongly favoured policy changes to existing fibre allocation programs and the creation of quota systems, over developing business relationships with organizations able to supply fibre.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".