Operating performance of passive infrared counters under different seasons
Bibliographic record
Abstract
This research analyzes the operating performance of two commercially available passive infrared counters (PICs) of pedestrians as a function of site, summer, fall and winter seasons in terms of counter sensitivity. Three sites were selected for field analysis in Winnipeg, Canada. Based on a sample of 24,690 people counted by the two PICs from July 2014 to February 2015, this research found that with a 95 percent confidence, Eco-Counter’s sensitivity ranged from 73 to 97 percent while TRAFCO’s ranged from 57 to 97 percent related to people occlusion. On weekdays, Eco-Counter’s absolute error was 16 percent and TRAFCO’s was 18 percent. On weekends, Eco-Counter’s absolute error was 18 percent and TRAFCO’s was 21 percent. In addition to people occlusion, site, seasons, and time of week (weekday and weekend) were found to affect the operating performance of the PICs. Correction factors were also calculated per counter, site, and seasons.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".