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Record W2520743645

Efficiency analysis of the Canadian wood-product manufacturing subsectors: A DEA approach.

2007· article· en· W2520743645 on OpenAlexaboutno aff
Saba Vahid, Taraneh Sowlati

Bibliographic record

VenueForest Products Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPulpwoodVeneerLoggingHardwoodWood industryProduct (mathematics)BusinessForestryIncentiveAgricultural scienceEngineeringPulp and paper industryOperations managementEnvironmental scienceAgricultural economicsMathematicsGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

The current state of hardwood log merchandising and bucking practices in West Virginia was examined by on-site interviews with 50 timber harvesting companies. Results indicate that most of the roundwood harvested was merchandized into sawlogs, pulpwood, OSB, peelers, veneer, and scragg. The average daily production of the logging companies was 4.4 truckloads per day with more than 60 percent of the companies producing 1 to 3 truckloads per day. Most sawmills required a small-end diameter between 10 and 12 inches and preferred logs 10 feet in length. Incentives ranging from $10 to $50 per thousand board feet (MBF) in Doyle scale would be needed for some companies to buck logs for grade. Approximately half of the logging companies and 46 percent of the independent loggers were willing to take advantage of training for better bucking decisions and strategies.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.061
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.012
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.011
GPT teacher head0.202
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2007
Admission routes1
Has abstractyes

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