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Optimize by Experimental Optimization Techniques on the Structure of Corrugated Bionic Needle

2010· article· en· W1833169177 on OpenAlexvenueno aff
Yan Li, Xin Qi, Qian Cong

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBionicsRipplePolynomial regressionReduction (mathematics)PolynomialQuadratic equationControl theory (sociology)Quadratic functionEngineeringRegression analysisMathematicsStructural engineeringBiological systemComputer scienceMathematical analysisGeometryStatisticsArtificial intelligenceElectrical engineeringBiology

Abstract

fetched live from OpenAlex

From the view of bionic, optimizing and engineering the corrugated bionic needle with polynomial regression. The results showed that; the width of the ripple with quadratic effect plays a major role in reducing resistance; the impact of non-smooth structure’s spacing could be ignored; when the width of the ripple is 1.75mm and the spacing is 1.00mm, the effect of reducing the resistance was the best. Key Words: Bionics; Polynomial regression; Mechanism of resistance reduction

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 categoriesScience and technology studies
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 score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.003
Scholarly communication0.0000.005
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 head0.004
GPT teacher head0.267
Teacher spread0.263 · 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.

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

Citations1
Published2010
Admission routes1
Has abstractyes

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