AMONGST SIX CANADIAN MUNICIPALITIES!!!
Bibliographic record
Abstract
Light pollution is broadly defined as the unnecessary illumination of the nocturnal environment. Light pollution is a pervasive phenomena shown to have harmful consequences for both the biotic and abiotic components of an ecosystem. While some municipalities have begun to address the environmental and economic costs of light pollution, most have not. The goal of this study was to investigate current municipal night lighting practices for six selected Canadian municipalities with the aim of determining their policies and practices for night lighting. Semi-structured interviews with key informants were conducted and analyzed using a mixed methods approach that included a thorough literature review. The results indicate that rising energy costs, aging infrastructure and the lighting industry are driving the majority of changes taking place in adapting municipalities while most municipalities remain content with status quo. The research conducted led to guideline improvements for municipal night lighting in today’s municipalities. Acknowledgements When I first arrived on campus, after moving to Guelph to join the MLA program over a weekend, I was welcomed into the LA building by a cheerful academic who was not afraid to joke with me, even at such an early hour. When arranging to meet with my advisor later in the week I
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".