Hypothetical Strategies to Reduce the Greenhouse Gas Emissions from Mobile Sources on the Orizaba Valley
Bibliographic record
Abstract
The Intergovernmental Panel on Climate Change established clear and solids conclusions on the 2013 report, it says that has been scientifically demonstrated with 95% of certainty, that human activities are the main cause of the global warming, observed since the middle of the XX century. The Orizaba Valley is a Mexican region, located at the geographic center of Veracruz State, having Orizaba City as the main demographic population surrounded by other municipalities, becoming the fourth metropolitan populated area of Veracruz State. This region has the third position on economic, historic and cultural relevance at Veracruz State, just after the Veracruz Port and Xalapa City. It was one of the main places with a vast economic growing during the Viceroyalty of the New Spain, being an obligatory passing route and resting place between Veracruz Port and Mexico City. This project estimates the magnitude of the Greenhouse Gas emissions coming from mobile sources at the Orizaba Valley. It includes the urban region of the municipalities of Ixtaczoquitlan, Orizaba, Río Blanco, Camerino de Mendoza and Nogales. The collected data was processed according to the Intergovernmental Panel on Climate Change methodology and it was possible to make the following projections: 1) One baseline scenario and 2) Three scenarios under hypothetical mitigation strategies that promise to achieve a reduction of GHG emission of 30 % from the year 2020 to 2050. Beyond this, also there is a significant reduction in fossil fuels consumption due to the efficient use of energy. All projections were made by using the Long-range Energy Alternatives Planning system software. In addition of the achievement on the GHG emissions reduction goal, it is possible to glimpse an economic recovery, if and only if, the decision makers of the governments decide to participate in the international trade of carbon market.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".