Journal of Sustainable development, Vol. 1, No. 3, November 2008, all in one file
Bibliographic record
Abstract
Natural light is a vital force for human beings.Successful daylighting in buildings requires trade-offs and optimization between competing design aspects (e.g.light distribution, glare, solar gains, views, etc.), whilst also including consideration of façade layout, space configuration, internal finishes and choice/operation of shading devices.However, to design energy-sustainable built environments which are conducive to human health, these variables have necessarily to be related also with biological and behavioural factors such as metabolic rhythms, psychological stimulation and occupants' preferences.Basing on a multidisciplinary review of existing literature, this paper looks at the relationship between quantitative physical measures of the luminous environment (e.g.horizontal and vertical illuminance, luminance ratio, correlated colour temperature), qualitative aspects of vision (e.g.uniformity, distribution), and psycho-physiological human response to natural light.The aim of the study consists in defining a framework to implement existing daylighting practices basing not solely on photopic requirements but also containing awareness of the demands for psychological and photobiological stimulation, so as to positively influence the health of occupants whilst enhancing energy savings.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.370 | 0.180 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".