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Record W2027789456 · doi:10.1177/193758670900200408

Incentivizing the Daylit Hospital: The Green Guide for Health Care Approach

2009· article· en· W2027789456 on OpenAlex
Ray Pradinuk

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHERD Health Environments Research & Design Journal · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsDaylightDaylightingHealth careWork (physics)IncentiveArchitectural engineeringProcess (computing)Evidence-based designEfficient energy useEnvironmental designSustainable designBusinessProcess managementEngineeringComputer sciencePolitical scienceSustainabilityEconomicsCivil engineering

Abstract

fetched live from OpenAlex

Daylight is usually mentioned immediately after reduced energy use in conversations about sustainable building design, yet in North America, daylight remains the most-asked-for/least-delivered aspect of the caregiver work environment. In Europe, essentially the same care practices continue to be accommodated in daylit building configurations. Recognizing that the daylighting credits in Leadership in Energy and Environmental Design (LEED) for new construction (NC) are more difficult for large healthcare projects to achieve during the design process, and that two daylighting credits are probably not enough of an incentive to change North American healthcare design practice, the Green Guide for Health Care (GGHC) has both simplified the process of calculating daylight achievement levels and increased the number of daylighting credits available from two to five.

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.

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)0.014
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.695
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.102
GPT teacher head0.398
Teacher spread0.296 · 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