Contributions to Keep the Atmosphere Balanced: How a MagneticMinimizer of Emissions from Mobile Sources Should Be Designed
Bibliographic record
Abstract
This paper is the first of an integrated set of documents intended to contribute to reduce air pollution controlling CO2 emissions and consequently helping to keep Carbon Cycle Balance. It is a selfless, committed and responsible contribution with technical information and experiences of 11 years of work with magnetic minimizers of emissions from mobile sources, which could facilitate synergistic work, by integrating facts whose importance could be going unnoticed, rather than favoring the environmental controversy. The main goal of this first paper is to disseminate a system of procedures to design a magnetic efficient and balanced minimizer of gases emissions from mobile sources. Such a minimizer must allow to satisfy the commitments made by most countries in the world to strengthen their global response to the threat of climate change, especially the Paris Agreement signed in April of 2016 by 195 countries engaged in an internationally coordinated effort to tackle climate change, by controlling global warming. The system of Procedures is the result of several years of experimental work and is supported by comparisons of Single Day Tests results on cars, without a minimizer and then after installing it, obtained in Colombia's ADC, in 2008 with a magnetic minimizer with hydraulic pretreatment and in 2018 with a magnetic minimizer without pre-treatment, using standard gasoline. When increases of CO2 emissions, for 3 tested cars in 2008 were compared with the correspondent 0.7% increase of the car, tested in 2018, this was found lower, in factors of 3.1, 5 and 2.1. With the minimizer without pre-treatment installed in this car, emissions reductions of 68.4% in CO and 12.5% in HC, meeting the standards, were found. This fact opens the option of designing a magnetic efficient and balanced minimizer; reducing CO and HC emissions controlling CO2 emissions, helping transport sector decarbonization and meeting international commitments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".