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Record W3134088819 · doi:10.21838/uhpc.9673

UHPC - Allowing for the Development of Float Homes in Ontario

2019· article· en· W3134088819 on OpenAlexaboutno aff
Lea Marshall

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsFloat (project management)RunwayHullFloat glassEngineeringCivil engineeringArchitectureForensic engineeringMarine engineeringGeography

Abstract

fetched live from OpenAlex

Based on the growing concern of increased water levels due to climate change, an interest in developing float homes has arisen. Examining case studies shown in British Columbia, where float home developments are common, it can be noted that float homes can be a successful investment. In British Columbia, the standard base construction is made of Styrofoam encased with reinforced concrete. This assures that the homes will not sink due to the deterioration of the reinforcement in the concrete. The European standard is to have open hull areas with which the home is built upon. It is critical to design an open hull base to improve the usage of space within the home. By incorporating an open hull there is additional storage space, room for mechanical equipment, and improved ballast in the structure. This paper aims to document the development of an Ultra High Performance Concrete (UHPC) prototype for the float foundations. Working in partnership with FDN Engineering, the objective of Facca Incorporated is to design and develop a foundation based on European examples. Facca Incorporated currently owns marina property that they intend to make into a float home community. The objective is to construct a prototype in 2019 and subject it to the ice conditions in Ontario, Canada. This paper will examine the history of float home construction and how UHPC can alleviate many of the problems the float homes are subject to.

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 score0.322
Threshold uncertainty score0.998

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.201
Teacher spread0.189 · 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
Published2019
Admission routes1
Has abstractyes

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