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Abrasive Wear of Geometrical Surface Structures of Scapharca Subcrenata and Burnt-end Ark Against Soil

2010· article· en· W1889468159 on OpenAlexvenueno aff
Rui Zhang, Zhili Lu, Jianqiao Li

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsAbrasiveShell (structure)Materials scienceComposite materialBiologyAnatomy

Abstract

fetched live from OpenAlex

Scapharca subcrenata(Arca subcrenala Lischke)and Burnt-end Ark (Arca inflata Reeve) were selected as the research object. The abrasive wear experiments of three types of surface structures against soil were performed in the abrasive tester. These surface structures include the Scapharca subcrenata node rib pattern shell, Scapharca subcrenata rib pattern shell and Burnt-end Ark. The test results showed that the wear-resistant function of the surface structures of the Scapharca subcrenata node rib pattern shell and the Burnt-end Ark shell was better than that of the surface structure of the Scapharca subcrenata rib pattern shell when the relative sliding velocity was 2.41m/s. When abrasive size was range from 0.380mm to 0.830mm, the wear loss of these three types of surface structures were increased with the relative sliding velocity increasing.Keywords: Scapharca subcrenata; Burnt-end Ark; geometrical surface structure; abrasive wear; wear resistance

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 categoriesMeta-epidemiology (narrow), Science 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 score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.004
Scholarly communication0.0000.006
Open science0.0010.000
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 head0.274
Teacher spread0.270 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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