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Record W4288096486 · doi:10.30682/aa2208f

To make it even better

2022· article· en· W4288096486 on OpenAlexfundno aff
Anne Isopp

Bibliographic record

VenueARCHALP · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology, Conservation, and Geographical Studies
Canadian institutionsnot available
FundersInternational Society of Oncology Pharmacy Practitioners
KeywordsArchitectural engineeringOrder (exchange)Computer scienceConstruction engineeringEngineeringBusiness

Abstract

fetched live from OpenAlex

The Vorarlberg architect Hermann Kaufmann is a pioneer of the modern timber construction with which his name is inseparably linked. In his pleasant but persistent manner, Hermann Kaufmann has never tired of pointing out the qualities of timber construction and, at the same time, developing it further with his buildings through the use of new modern products and the sounding out of new construction methods in order to make timber construction, as he himself says, even better. He has always been open to new developments. Commercial buildings seem to be a good field for trying out these new developments and new construction methods in timber construction. As functional and practical as commercial buildings need to be, they also clearly do leave some leeway for trying out new ideas. Clients, architects, and construction companies use such buildings to experiment, to try out new joints, new material combinations and new engineered woods. By looking at a few examples of industrial developments, we can see how timber construction has changed from a traditional to an ultra-modern method, and better understand the advantages that this building material offers and how Hermann Kaufmann prepared this path with his buildings. This text is an abridged and revised version of an article first published in «Bauband 3: Gewerbebauten in Lehm und Holz», a special edition of the journal DETAIL – Zeitschrift für Architektur + Baudetail.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.014
GPT teacher head0.228
Teacher spread0.215 · 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 teacher head, not a consensus.

Study designObservational
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueARCHALPSame topicEcology, Conservation, and Geographical StudiesFrench-language works237,207