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Record W1997012187 · doi:10.5558/tfc791107-6

Solid wood supply impediments for secondary wood producers in British Columbia

2003· article· en· W1997012187 on OpenAlexaffvenueabout
Robert Kozak, Thomas C. Maness, Tim Caldecott

Bibliographic record

VenueThe Forestry Chronicle · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProcurementBusinessSupply chainGrading (engineering)CommodityQuality (philosophy)Competition (biology)Agricultural economicsMarketingEngineeringEconomicsCivil engineering

Abstract

fetched live from OpenAlex

Many nations are attempting to strengthen the development of secondary wood manufacturing in an attempt to cope with job losses due to declining annual allowable cut levels, increasing global competition and decreasing prices of commodity lumber products. This paper describes the results of a survey conducted in the province of British Columbia aimed at uncovering the impediments to wood supply relationships between secondary and primary manufacturers. The study looks at the raw material needs of different categories of secondary manufacturers pertaining to wood quality, price and service and highlights similarities and differences between these groups. In general, the study found that the majority of secondary manufacturers are experiencing lumber procurement problems. Lumber grading issues are the biggest overall concern of manufacturers, and manufacturers of engineering building components were found to experience the most difficulties. The paper concludes with a discussion and recommendations to improve synergies between primary and secondary wood producers in British Columbia. Key words: value-added wood products, wood quality, supply chain management, customer-oriented manufacturing

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.256
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2003
Admission routes3
Has abstractyes

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