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Record W4391559982 · doi:10.21203/rs.3.rs-3877656/v1

An experimental study on the estimation of the static characteristics and relative displacement of the surfaces of an 88% tin-coated plain bearing

2024· preprint· en· W4391559982 on OpenAlexfundno aff
Mehala Kadda, Bendaoud Nadia, Boukhatmi Hadi, Bendaoud Mohamed Habib

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
FundersInstitute of GeneticsCentre National de la Recherche Scientifique
KeywordsTinDisplacement (psychology)Bearing (navigation)GeologyPlain bearingGeotechnical engineeringGeodesyMaterials scienceComposite materialComputer scienceMetallurgyArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Abstract In order to prevent warming and deterioration in the surface condition of the plain bearing (shaft/bearing), it was necessary to enhance the heat engines' efficiency by applying a coating containing 88% tin to the internal surface of the bearing. To assess the effectiveness and reliability of these coatings, a testing rigs specifically designed for automotive engine bearings was developed. The University of Poitiers collaborated on the experimental analysis, which explored the impact of different loads and speeds. The results of the analysis confirmed that frictional efficiency tended to improve with low loads and high speeds. The displacement observed in the bearing decreased as the speed increased, particularly in lightly loaded plain bearings. Conversely, heavily loaded sliding bearings exhibited higher displacement values. At the 315º position, the displacement was particularly significant and increased significantly with the load, while it was less significant at the 45º position. In the 315º position, the circumferential face displacement of the shaft decreased notably with increasing speed at low loads. Similarly, for higher loads, the displacement only slightly decreased with increasing speed, but these displacements were very high at the 315º position.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.358

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 integrity0.0000.001
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 head0.042
GPT teacher head0.363
Teacher spread0.321 · 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
Published2024
Admission routes1
Has abstractyes

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