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Record W2134539866 · doi:10.1061/9780784412329.108

A Special Purpose Simulation Template for Modeling Tire Usage of Mining Truck Fleet

2012· article· en· W2134539866 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConstruction Research Congress 2012 · 2012
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsStantec (Canada)University of Alberta
Fundersnot available
KeywordsTruckComputer scienceSupply and demandTransport engineeringAutomotive engineeringOperations researchEngineering

Abstract

fetched live from OpenAlex

The logistics for the supply of tires for trucks utilized in mining operations are generally affected by their demand and supply on the market. In times of high demand and tire scarcity, operations of most companies with large truck fleets are affected due to inadequate analysis and tire usage planning. The lack of a proper tool for practitioners to use for this purpose has contributed to the problem. This study proposes a special purpose simulation template that can be used to solve the problem. The template was developed for analyzing a six tire truck because it is the most common truck type used in mining operations. It utilizes statistical distributions fitted to historic field data of tire usage, and outputs the most likely number of used, early failed and worn out tires for the analyzed period. A simulation based approach was adapted because of the dynamic and random nature of the tire usage problem which does not lend itself to analytical solutions.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.567

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 communication0.0000.001
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 head0.115
GPT teacher head0.359
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