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Record W7133034335

Massive Wood Construction in Finland : Past, Present, and Future

2019· other· en· W7133034335 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioBoard Library Catalog (Open Research Library) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsApartmentQuarter (Canadian coin)Product (mathematics)PopularityArchitectureVeneerEngineered woodSolid wood
DOInot available

Abstract

fetched live from OpenAlex

Finland has a long history of massive wood construction such that the log construction technique has been used as a traditional method of Finnish residential construction for thousands of years, and the entire history of Finnish architecture is based on this technique. Today, almost all leisure buildings, for example, cottages in Finland are made of wood and mostly log construction. Also, today 90% of Finland’s detached houses have timber frames, and a quarter of them are made from industrial glue logs. Apartment buildings began to be made of wood, especially cross-laminated timber (CLT) and laminated veneer lumber (LVL). The most common way of constructing wooden apartments is to use volumetric elements as compared to load-bearing large elements and post-beam systems. The increase in environmental awareness in Finland, as in many European countries today, strengthens the popularity of wood construction, and this brings the search for innovative and environmentally friendly engineered wood product solutions (e.g., dovetail massive wood board elements) as a future vision. The chapter aims to identify, combine, and consolidate information about massive wood construction in Finland from past, present, and future perspectives. This study will assist and guide Finnish key professionals in the design and implementation of timber buildings.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Bibliometrics, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0350.024
Science and technology studies0.0000.002
Scholarly communication0.0050.016
Open science0.0050.010
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0270.014

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 head0.039
GPT teacher head0.314
Teacher spread0.274 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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