Applicability of daylighting computer modeling in real case studies: Comparison between measured and simulated daylight availability and lighting consumption. Building and Environment
Bibliographic record
Abstract
this paper is published in / Une version de ce document se trouve dans : IEA (International Energy Agency) Task 21 / Annex 29: Daylight in Buildings, November 1998, 68 pp. www.nrc.ca/irc/ircpubs NRCC-42862 Subtask C: Daylighting Design Tools T21/C1-21/CAN/98-11 Applicability of Daylighting Computer Modeling in Real Case Studies: Comparison between Measured and Simulated Daylight Availability and Lighting Consumption A report of IEA SHC Task 21 / IEA ECBCS Annex 29 Daylight in Buildings Subtask C: Daylighting Design Tools Report T21/C1-21/CAN/98-11 Applicability of Daylighting Computer Modeling in Real Case Studies: Comparison between Measured and Simulated Daylight Availability and Lighting Consumption by Anca D. Galasiu * Morad R. Atif * A report of IEA SHC Task 21 / IEA ECBCS Annex 29 * National Research Council Canada, Institute for Research in Construction, Indoor Environment Research Program Subtask C: Daylighting Design Tools Report T21/C1-21/CAN/98-11 IEA Solar Heating and Cooling Programme (IEA SHC) The International Energy Agency (IEA) was established in 1974 as an autonomous agency within the framework of the Economic Cooperation and Development (OECD) to carry out a comprehensive program of energy cooperation among its 24 member countries and the Commission of the European Communities. An important part of the Agency's program involves collaboration in the research, development and demonstration of new energy technologies to reduce excessive reliance on imported oil, increase long-term energy security and reduce greenhouse gas emissions. The IEA's R&D activities are headed by the Committee on Energy Research and Technology (CERT) and supported by a small Secretariat staff, headquartered in Paris. In addition, three Working Parties are charged with monitorin...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".