Effects of light emissions from LCD monitors on public health
Bibliographic record
Abstract

 Background: Evidence suggests that exposure to prolonged use of computer monitors may lead to eye discomforts such as eyestrain, blurred vision, headaches, as well as computer vision syndrome. With the increase of internet and computer use at school and work, it raises a serious concern on whether or not the users are affected negatively. Objectives: The following study focuses on estimating the risk of using computers at the British Columbia Technology of Institute (BCIT) laboratories to determine if there is sufficient room lighting (illuminance) and monitor brightness (luminance) to ensure the safety of students at the school. Methods: Illuminance and luminance were both measured by means of a calibrated Unfors Xi light detector connected to a base unit. Several areas of each environment and monitor were measured and average values were taken to ensure that the data was reliable. The data were compared to applicable standards to determine if room lighting and computer brightness at BCIT were appropriate for safe student use. Results: The average illuminance was measured to be 405.4 Lux when the recommended level is 500 Lux. The results are considered statistically significant (p-value = 2x10-6). On the other hand, the luminance measurements averaged a reading of 94.6 cd/cm2 when the recommended minimum luminance level is 80 cd/cm2 (p-value = 0.99964). Discussion: The collected data show that the illuminance inside the BCIT laboratories where testing was conducted is not sufficient while the luminance of computer screens was found acceptable. Conclusion: The measurement of the illuminance inside BCIT laboratories showed that room lighting was insufficient and alternations of the monitor's brightness were made to compensate for insufficient lighting. It is recommended to increase room lighting in the tested laboratories in order to have all areas of the laboratory sufficiently lit at all times. This is to ensure that all students at BCIT are provided with a safe and healthy environment for computer use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".