Improving passive solar collector for fiber optic lighting
Bibliographic record
Abstract
Increasing energy demand and cost are the main motivators for this research project, which utilizes solar radiation for lighting interior spaces in buildings. The objective is to use passive solar radiation collection coupled with fiber optic bundles to provide natural lighting. The research team proposes a novel passive fiber optic lighting system design, which collects the abundant supply of free solar radiation without using expensive tracking systems that require constant alignments year-round. The passive lighting system design will respond to several criteria. The exposed collector dome is fixed and capable of collecting as much light as possible on an average sunny day and robust to withstand all weather conditions. The fiber optic cable will deliver light up to 20 m or to the basement of a two story house; delivered light intensity must satisfy standard domestic lighting codes. The research focuses on two aspects of the system: the solar light collector and fiber optic distribution cables. In the initial project phase the dome of a commercially available tubular daylighting device is modified to enhance light collection. Preliminary tests indicate that it will be possible using gratings to redirect more sun rays into the tubular daylighting device, increasing the light captured. The next modification enables fiber optic bundles to be coupled to the system for light distribution to areas in a building. A parabolic mirror pair set-up as a compound concentrator inside the tubular daylighting device will concentrate light into the fiber bundle at the base.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".