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The Technology Study of Camshaft Treated by Laser Bionic Melting Process in Aqueous Media Cooling

2010· article· en· W1484247414 on OpenAlexvenueno aff
Yu Zhao, Dong-Xue Song, Luquan Ren, Li Chen

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCamshaftCoupling (piping)Materials scienceAqueous mediumLaserMetallurgyWear resistanceComposite materialProcess (computing)Mechanical engineeringAqueous solutionEngineeringOpticsComputer scienceChemistry

Abstract

fetched live from OpenAlex

Biomimetic coupling wear resistance model was created according to biomimetic coupling phenomenon and biomimetic coupling theory, utilize the preparation of aqueous media by laser cooling techniques, process biological coupling unit on the gray cast iron surface according to the organism form, make the bionic coupled unit in accordance with the laws of organism combination, form bionic coupling surface which is similar to the organism form with material surface to improve the wear resistance of the camshaft. Studies show, the region which dealt with aqueous media by laser can get small grains, and grain size is uniform, hardness and wear resistance is improved. Key words: bionic coupling; laser melting; camshaft

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 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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.009
Science and technology studies0.0000.002
Scholarly communication0.0000.003
Open science0.0020.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.272
Teacher spread0.269 · 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 teacher head, 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

Citations0
Published2010
Admission routes1
Has abstractyes

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