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Record W4394433145 · doi:10.6084/m9.figshare.1601881

Contrasting the differences in the micro-environment of green and conventional roofs in Toronto, Canada.

2015· dataset· en· W4394433145 on OpenAlexaboutno aff
Alessandro Filazzola, Ecoblender

Bibliographic record

VenueFigshare · 2015
Typedataset
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEnvironmental scienceArchitectural engineeringEngineering

Abstract

fetched live from OpenAlex

Purpose: To see how temperature, soil moisture, and light availability differ between green and conventional rooftops at York University, Toronto. Hypothesis: Green roofs ameliorated the microclimate of a building by absorbing solar radiation, resulting in cooler temperatures and less evapotranspiration. To compare how green roofs may alter the temperature, evapotranspiration or solar radiation, we placed four sets of three Parrot Flower Power sensors in 6” pots filled with soil. Each set of pots and Flower Power sensors were be placed on two green roofs and two non-green roofs at York University. These four roofs were surveyed from September 16th to November 9th and data collected periodically. Soil moisture changes within the pots was used as a proxy for the evapotranspiration rate on top of the roofs. Data points were extracted for each day using the software Web Plot Digitizer. One set of loggers on a tradional roof did not record for the duration of the season.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.263
Threshold uncertainty score0.995

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)0.0060.000

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 head0.024
GPT teacher head0.224
Teacher spread0.200 · 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
GenreDataset

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
Published2015
Admission routes1
Has abstractyes

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