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Record W4386698887 · doi:10.32920/24084825.v1

A pathway to rooftops: a feasibility study of green roof typologies at 105 Bond Street

2023· preprint· en· W4386698887 on OpenAlexaffabout
Jack Douglas Lawson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsCarleton UniversityToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsGreen roofRoofNexus (standard)StormwaterBondGreen infrastructureUrban planningBusinessArchitectural engineeringCivil engineeringEnvironmental planningEnvironmental scienceEngineeringFinanceEcology

Abstract

fetched live from OpenAlex

This paper examines the feasibility of a green roof at the School of Urban and Regional Planning (SURP), located at 105 Bond Street, Toronto. Green roofs have proven benefits within the energy-air-stormwater nexus that often translate in direct operating cost savings. With a history of prior proposals, SURP is well positioned both ideologically and practically for the implementation of a green roof. Ryerson University is also undergoing a period of vertical growth which will dramatically increase visibility on existing heritage structures like 105 Bond. Ultimately, this paper proposes a semi-intensive green roof split across the building’s upper and lower tiers. Over time the School of Urban and Regional Planning will continue to develop its robust culture of custodianship among faculty, students, and alumni to allow for the long-term health of the green roof and reduced on-going maintenance load. The addition of dedicated research beds included on the upper tier of the proposed green roof are another key long-term benefit that will produce year-to-year project outcomes. Key words: Green roof, resiliency, Canada, planning, vegetation

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.617

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.072
GPT teacher head0.292
Teacher spread0.219 · 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 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
Published2023
Admission routes2
Has abstractyes

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