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Record W3195592478 · doi:10.31590/ejosat.952696

Gözlük Çerçevesi Üretimi İçin Otomatik Bir Sistem Geliştirme

2021· article· tr· W3195592478 on OpenAlexaff
Rıza İLHAN

Bibliographic record

VenueEuropean Journal of Science and Technology · 2021
Typearticle
Languagetr
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsStantec (Canada)
FundersEge Üniversitesi
KeywordsPhysicsHumanitiesArt

Abstract

fetched live from OpenAlex

Gözlüğün çerçevesi tipik olarak el aletleri kullanılarak adım adım yapılır. Bu süreç zaman alıcı, maliyetli ve hassas üretim için daha az doğru sonuçlar vermektedir. Bu makalede, gözlük çerçevesini üretmek için otomatik bir sistem sunmaktayız. Üretimde mekanize edilerek seri üretimde daha fazla ürün üretilmesi hedeflenmektedir. Yerli endüstriyel aparatların tasarımı ve üretimi bu amaç doğrultusunda çalışmalarını sürdürmektedir. Önerilen sistem robotik bir kol ve bir aparattan oluşmaktadır. Altı serbestlik derecesine (DOF) sahip robotik el kaynak için ve aparat çerçeve parçalarını sabit bir konumda tutmak için kullanılmaktadır. Elde edilen sonuçlara göre, sistem kabul edilebilir performans göstermiş olup, mevcut üretim sistemleri ile ilgili eksiklikleri giderebilecek kapasiteye sahip sonuçlara ulaşılmıştır.

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)0.001
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0220.008

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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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
Published2021
Admission routes1
Has abstractyes

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