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Record W3179935407 · doi:10.14288/1.0397262

Greening the Marina : A Roadmap to Renewable Energy for the Clubhouse at Spruce Harbour Marina

2021· article· en· W3179935407 on OpenAlexaboutno aff
Natasha Harland, Natalie Varga, Lauren Kummer, Wang Haoyue

Bibliographic record

VenuecIRcle (University of British Columbia) · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyHarbourGreeningEnvironmental scienceFisheryEcologyBiologyComputer science

Abstract

fetched live from OpenAlex

The energy industry is currently responsible for 78% of greenhouse gas emissions in Canada. With the climate crisis worsening, it is crucial that carbon-intensive energy sources are transitioned to renewable and low-carbon sources to mitigate greenhouse gas emissions and prevent further climate change. This project provides the research and resources necessary to adopt renewable energy technologies for the floating clubhouse at the Spruce Harbour Marina (SHM) in Vancouver, B.C. Spruce Harbour Marina is home to the Greater Vancouver Floating Home Co-operative (GVFHC), a nonprofit live-aboard marina community in the False Creek neighbourhood. This report recommends renewable energy technologies for the GVFHC to install, based on a set of weighted decision criteria and a cost-benefit analysis which accounts for the following project objectives: To identify the most viable renewable energy technologies for providing hot water heating and space heating for the clubhouse at Spruce Harbour Marina. To create a feasibility report that provides sufficient information for a grant application to fund the design and implementation of the proposed energy technologies. To provide a list of steps (a “roadmap”) to decrease the energy usage and install renewable energy technologies at the Marina.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.171
Teacher spread0.155 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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