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Record W2133197669 · doi:10.3968/7083

Pin on Disc Wear volume Prediction Based on Grey System Theory

2015· article· en· W2133197669 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicGrey System Theory Applications
Canadian institutionsnot available
Fundersnot available
KeywordsVolume (thermodynamics)LubricationMechanicsMaterials scienceMathematicsEngineeringMechanical engineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

This paper is a study of pin on disc wear volume, with the MMW-1A vertical friction and wear testing machine as the testing equipment, under different lubrication conditions. In this paper, the pin wear volume GM(1,1) prediction model is built based on the grey system theory, GM(1,1) the model consists of a single variable in the first-order differential equation. The pin wear volume measured compare with GM(1,1) predicted wear volume, The comparison results showed that, the predicted values by the GM(1,1) are very close to the experiment measured values, and the precision of predicted results is quite high.

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)0.026
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.017
Science and technology studies0.0010.006
Scholarly communication0.0010.011
Open science0.0060.000
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 head0.029
GPT teacher head0.367
Teacher spread0.338 · 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