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Record W4402487924 · doi:10.1080/00084433.2024.2399879

Demonstration of a novel jaw crusher design at a lab scale

2024· article· en· W4402487924 on OpenAlexafffund
Eduard Guerra, Greg Lakanen

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2024
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsLaurentian University
FundersIAMGOLD
KeywordsCrusherScale (ratio)Engineering drawingProcess engineeringEngineeringComputer scienceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

A novel jaw crusher was designed, constructed and tested at a lab scale. Differences from a conventional jaw crusher include a swing plate that is supported at the outlet, rather than the inlet, wear plates with a corrugation pattern that are designed to break the ore in bending rather than compression, and a trapezoidal wear plate shape to allow for swelling of broken ore. The crusher was tested using an ore sample that was mainly composed of feldspar. The performance of the crusher was evaluated by measuring changes in the size distribution of the ore and the corresponding electrical energy consumed, as a function of ore feed rate. The results indicated that the crusher product size distribution was narrower than that of the feed and independent of ore feed rate, with relatively little generation of fines, demonstrating that the crusher performed as designed in terms of ore breakage mechanism. Though calculated breakage efficiencies suggest that the crusher may be more energy efficient than a conventional jaw crusher, the use of an undersized crusher motor and possible deficiencies in the design of the transmission mechanism, prevent any concrete conclusions from being drawn.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.216
Teacher spread0.196 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes2
Has abstractyes

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