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Supporting Wood Supply Chain Decisions With Simulation For A Mill In Northwestern Bc

2003· article· en· W2408578903 on OpenAlexaffvenue
Jason Myers, Evelyn W. Richards

Bibliographic record

VenueINFOR Information Systems and Operational Research · 2003
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsTruckMillYardDiscrete event simulationSupply chainOperations managementEnvironmental scienceSimulation modelingComputer scienceOperations researchBusinessAutomotive engineeringSimulationEngineeringEconomics

Abstract

fetched live from OpenAlex

Seasonal restrictions on harvesting and transportation operations have traditionally forced mill managers to maintain high levels of inventory in the log-yard to ensure continuous operation of the mill. We propose two technologies which may prolong the operating season, potentially reducing inventory handling and holding costs: Central tire Inflation (CTIS) and cable-based harvest systems. To evaluate the effect of the CTIS-equipped trucks and cable-based harvesting on the wood supply chain, a discrete event simulation model was developed and was validated. The simulation model estimates the total cost of the system with each technology included, both separately and in combination. It was found that there was a small significant saving in inventory costs lor scenarios that used CTIS trucks, but no significant savings in overall cost was observed for any of the technologies.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.043
GPT teacher head0.335
Teacher spread0.292 · 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 designSimulation or modeling
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

Citations10
Published2003
Admission routes2
Has abstractyes

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Same venueINFOR Information Systems and Operational ResearchSame topicForest Biomass Utilization and ManagementFrench-language works237,207