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Record W2988302899 · doi:10.36243/fmtu-2004.12

A természet és a tudomány néhány kompozitjának tribológiai összehasonlítása

2004· article· hu· W2988302899 on OpenAlexaff
Róbert Keresztes, Gábor Kalácska, Kalácska Margaret

Bibliographic record

VenueFiatal Műszakiak Tudományos Ülésszaka · 2004
Typearticle
Languagehu
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNAKTerm (time)PhysicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Currently, many of the most sought after timber species are not only scare in international markets but are alsó threatened with extinction.Guaiacum sanctum from the Family Zygophyllaceae (common names: Guayacán, Lignum vitae, Palo santo), was once a commercially valuable timber from the tropical dry forests of Central America.Later centuries with the increase in industry G. sanctum became prized for the unique mechanical and structural characteristics of its wood and suitability for preparing different machine elements.The study shows a tribological comparison (friction, wear) to some modern polymeric materials used for machine elements.It also gives a ranking for the tested materials. ÖsszefoglalásNapjainkban a legkeresettebb fafajták közül több nemcsak ritka lett a nemzetközi piacon, de a kihalásuk veszélye is fenyeget.A Zygophyllaceae családjába tartozó Guaiacum sanctum (ismertebb nevén Guayacán, Lignum vitae, Palo santo), valamikor kereskedelmileg értékes fafajta volt.A későbbi évek során azonban különleges mechanikai és szerkezeti tulajdonságai miatt az ipari fejlődés során a G. sanctum igen értékessé vált gépelemgyártási lehetősége miatt.A cikk korszerű polimer kompozitokkal hasonlítja össze a fa tribológiai jellemzőit, behatárolja elméleti alkalmazhatóságát napjainkban.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.006

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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2004
Admission routes1
Has abstractyes

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