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Record W2408454559 · doi:10.1016/j.ijmst.2014.11.006

Fundamental behaviours of production traffic in underground mine haulage ramps

2015· article· en· W2408454559 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Mining Science and Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsQueen's UniversityGolder Associates (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHaulageProductivityInefficiencyEngineeringProduction (economics)Automotive engineeringMining engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Ramps (or declines) are often used in underground mines to transport ore, waste, materials, and personnel. This paper studies mine ramp productivity and presents results from a set of computer simulations designed to model the fundamental behaviours of ramp haulage systems. Simulations show that, under fundamental assumptions without random disturbances, the haulage system always converges to a periodic behaviour in the steady state, but that productivities vary between equilibria. Simulations also demonstrate how productivity per vehicle does not necessarily decrease as more vehicles are added and, for example, in the five-vehicle case, how a 3.1% improvement can be achieved over the use of four vehicles. The result reveals the inefficiency of commonly-used lockout-style vehicle coordination strategies, and suggests a possible avenue for improving the productivity of haulage ramps by controlling the system to achieve more productive behaviours.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.266
Teacher spread0.243 · 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