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Record W4367554052 · doi:10.1061/9780784484777.027

Post + Panel: Next Generation Point Supported CLT Floors

2023· article· en· W4367554052 on OpenAlexaff
Carla Dickof, Md Shahnewaz, Christian Slotboom, Houman Ganjali, Thomas Tannert

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsSurgical Specialties (Canada)Quest University CanadaUniversity of Northern British Columbia
Fundersnot available
KeywordsShear (geology)Structural engineeringCross laminated timberPoint (geometry)PunchingShear strength (soil)Shear stressGeologyGeotechnical engineeringEngineeringMaterials scienceMathematicsComposite materialMechanical engineeringGeometry

Abstract

fetched live from OpenAlex

This study provides an overview of the ongoing Post + Panel research program on point-supported cross-laminated timber (CLT) floors. Recent studies on rolling shear and punching shear have found that the capacity of CLT in shear may be higher than those specified in the standards. However, while there is a compelling case for higher point supported strengths, most tests have been completed outside of North America, and limited tests have been completed on point supported CLT panels. The work presented herein addresses this critical research need and consists of CLT rolling shear testing, CLT punching shear testing, and full-scale point-supported CLT floor testing. Rolling shear values are based on both short- and long-term testing including testing using both bend and inclined tests. Rolling shear strength amplification factors and typical stress distributions around columns are presented from previous research in various column configurations. The rolling shear strength was determined for CLT with lamellas of different species, grades, and aspect ratios. The results show that the mean rolling shear strength of CLT was between 0.9 and 1.8 MPa.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.004

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 head0.069
GPT teacher head0.219
Teacher spread0.149 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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