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Record W3006427680 · doi:10.1002/dama.201900016

Einbruchhemmung mit Mauerwerk aus Leichtbeton

2020· article· de· W3006427680 on OpenAlexaff
Frank Alexander, Sandra Heinrichsberger, Thomas Kranzler, Jürgen Küenzlen, Norbert Sack

Bibliographic record

VenueMauerwerk · 2020
Typearticle
Languagede
FieldEngineering
TopicCivil and Structural Engineering Research
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsPolitical scienceArt

Abstract

fetched live from OpenAlex

Abstract Die Produktpalette von Mauersteinen aus Leichtbeton weist sowohl schwere, hochfeste Steine für Innenwände, Haustrennwände sowie zweischalige und zusatzgedämmte (WDVS) Außenwände als auch leichte, hochwärmedämmende Steine für monolithische Außenwände auf. Während die Montage einbruchhemmender Bauelemente in Mauerwerk aus schweren, hochfesten Steinen aus Leichtbeton seit jeher in DIN EN 1627 bis hin zur Widerstandsklasse RC 6 geregelt ist, war die Montage in leichten, hochwärmedämmenden Steinen für monolithische Außenwände aus Leichtbeton bislang nicht abgedeckt. Aus diesem Grund wurden durch das ift Rosenheim Untersuchungen zur Einbruchhemmung von hochwärmedämmendem monolithischem Leichtbetonmauerwerk durchgeführt. Für übliches, 365 mm dickes und mit einem Leichtputz Typ I verputztes Leichtbetonmauerwerk der Steindruckfestigkeitsklasse 2 und der Rohdichteklasse 0,40 wurde die in Deutschland von der Polizei empfohlene Einbruchwiderstandsklasse RC 2 nachgewiesen. Die Ergebnisse des Forschungsprojekts mündeten in einem Vorschlag für die Ergänzung der Tabelle NA.2 des deutschen Nationalen Anhangs zu DIN EN 1627.

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)0.000
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.223
Teacher spread0.205 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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