ECO-EFFICIENCY OF THE ROAD FREIGHT TRANSPORT OF THE REGION NORTH AMERICA IN THE INTERNATIONAL TRADE
Bibliographic record
Abstract
This research aims at quantifying and analyzing carbon footprint as an indicator of eco-efficiency in the Mexican international road freight transport industry. We compare Mexican efficiency to the United States and Canada. This evaluation is done using GHG emissions (Greenhouse Gas) of Quantis in its Scope 3 version. We examine data series for the period 2003-2011. The carbon footprint of Mexico, USA and Canada was calculated and then a comparative evaluation of the emissions was performed for each of the 3 countries. The results show that Mexico and Canada generate a higher percentage of indirect emissions, without downplaying direct emissions generated. In the US the total emissions from the sector study (80.62%) refers to emissions generated directly by operating units of the sector. It concludes with alternative solutions to mitigate emissions
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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 | 0.002 | 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".