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Record W2977961736 · doi:10.1002/cepa.652

Liquid Composites, der Schlüssel für hochwertige Glas‐Stein‐Laminate

2018· article· de· W2977961736 on OpenAlexaff
Christian Scherer, Jens Erdmann, A. G. K. Daniel, Ernst Semar, Thomas Scherer, Wolfgang Wittwer

Bibliographic record

Venuece/papers · 2018
Typearticle
Languagede
FieldEarth and Planetary Sciences
TopicBuilding materials and conservation
Canadian institutionsAtlantic School of Theology
Fundersnot available
KeywordsMaterials scienceComposite materialPolymer chemistry

Abstract

fetched live from OpenAlex

Traditionell versteht man unter Verbundgläsern Laminate, die aus mindestens zwei Glasscheiben bestehen und durch eine polymerbasierte, als Folie oder Flüssigkomponente eingebrachte Zwischenschicht verbunden sind. Die Zwischenschicht erweitert das Eigenschaftsspektrum von Glasprodukten erheblich. Das Flüssig‐Laminationsverfahren wird als Alternative zum Autoklavenprozess mit Folienmaterialien immer beliebter, da verschiedene technische und visuelle Anforderungen leichter oder auch überhaupt nur damit zu erreichen sind. Ein Anwendungsfeld sind Verbunde von Glas mit anderen Werkstoffen, beispielsweise mit Produkten wie Naturstein oder Keramik. Hier kommt der Vorteil zum Tragen, dass bei der Lamination mit Liquid Composites weder hohe Temperaturen noch erhöhte Drücke notwendig sind, unter denen die z.T. filigranen Steinplatten geschädigt werden können. Der vorliegende Beitrag beleuchtet die Entwicklung eines Liquid‐Composites‐Systems, mit dem beide Werkstoffe sowohl dauerhaft, bruchfest und in visuell einwandfreier Qualität zusammengefügt als auch anschließend im Verbund verarbeitet werden können.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.006

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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 designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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