Automobile Idling Reduction Program
Bibliographic record
Abstract
abstract: Description\nBy avoiding vehicle idling for three minutes every day of the year can reduce 1.4 million metric tons annually, which is equivalent to taking 320,000 cars off the road for the entire year (Canada.ca, 2016). The Automobile Idle Reduction Program (AIRP) is an outreach initiative to prevent carbon emissions from being released into the air by automobiles idling in Maricopa County. The initiative establishes a campaign to promote behavioral changes that target high idling industries: freight and delivery, schools and drive- thru facilities.\n\nBackground\nGlobally, carbon emissions negatively alter the air we breathe and is a leading cause in climate change. These problems adversely affect the global environment and human health. Additionally, they have cancer causing agents in the particulate matter. Unfortunately, over the years, Maricopa County has failed to meet air quality standards for particulate matter pollution which effects the health of residents. By not meeting the air quality standards, Maricopa County can receive sanctions and the Environmental Protection Agency can reject Arizona’s State Implementation Plan. This looming threat can financially impinge the economy of Maricopa County, potentially costing taxpayers a substantial increase in taxes.\n\nStrategy and Solution\nTo battle the creation of carbon emissions and particulate matter, AIRP has developed a strategy for each industry. In partnership with the Maricopa County Air Quality Department, AIRP will introduce the freight and delivery companies to the Diesel Emission Reduction Act (DERA) Grant promotion to facilitate and fiscally assist with changing older diesel engines into higher efficiency engines that burn cleaner. Provide educators a fifth to eighth grade state approved education program to teach students the importance of vehicle idling reduction at no cost. And work with community organizations to offer a discount at their stores for those patrons who choose to turn their engine off and order inside, rather than idling in the drive-thru facilities. The campaign will market the interest of AIRP to the general public through purposefully placed billboards, light rail wraps, social media pushes, handouts and vinyl stickers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.236 | 0.064 |
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".