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Research and Analysis of Dry Crushed Waste Tire Processing

2011· article· en· W2133399392 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

VenueApplied Mechanics and Materials · 2011
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsNickel Institute
Fundersnot available
KeywordsGrindingScrapMaterials scienceWaste managementMetallurgyEngineering

Abstract

fetched live from OpenAlex

For the treatment of waste tires and waste tire reuse has become an important task in today's society. Waste tire processing methods are: restructuring the use of the prototype. Energy use of waste tires as fuel will use high-temperature heating with thermal decomposition of waste tires, to promote its decomposition into oil, combustible gas, carbon. Scrap tire retreading. Powder production and other means. By mechanical means will be used after the tire tread and some other parts of the split will be obtained after crushing the powder material is powder. Currently the main mode of production has powder dry grinding, cryogenic grinding and wet grinding method. Different methods produce different particle size range of powder, powder surface morphology is also different. Dry grinding, wet grinding and cryogenic grinding of the legal system into a powder particle size in the range of 0.3 mm ~ 1.5 mm, 0.075 mm ~ 0.3 mm and 0.075 mm or less. Dry grinding method because of his production and processing simple process has been widely used in powder production process. For dry grinding process and mechanical work made for the research and analysis.

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 score0.642
Threshold uncertainty score0.325

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 communication0.0000.000
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 head0.024
GPT teacher head0.234
Teacher spread0.210 · 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