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Record W899774621

ZASTOSOWANIE METOD SZTUCZNEJ INTELIGENCJI DO KLASYFIKACJI WAD W ODLEWACH ZE STOPÓW Al-Si-Cu

2006· article· pl· W899774621 on OpenAlex
L. A. Dobrzański, M. Krupiński, J. H. Sokołowski

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueArchiwum Odlewnictwa · 2006
Typearticle
Languagepl
FieldEngineering
TopicSurface Treatment and Coatings
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPhysicsTheologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Praca dotyczy oceny jakości elementow np. korpusow silnikow samochodowych ze stopow aluminium wytworzonych metodą Cosworth, przy wykorzystaniu analizy obrazow zdjec cyfrowych uzyskanych w wyniku rentgenograficznych badan defektoskopowych tych ze elementow. Uzyskane wyniki decydują o tym, czy wytworzony produkt jest odpadem poprodukcyjnym czy tez nie, co ma na celu zmniejszenie liczby wytwarzanych produktow niespelniających kryteriow kontroli technicznej. W pracy przedstawiono opracowaną, komputerowo wspomaganą metode oceny jakości odlewow, na podstawie morfologii wad, przy zastosowaniu narzedzi sztucznej inteligencji.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.005

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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