Megacities and Climate Change in North America
Bibliographic record
Abstract
This article presents a summary of a broader study that compares the climate change plans of the three North American megacities—Mexico City, Los Angeles, and New York—plus Toronto. Because reaching a globally binding agreement to reduce greenhouse gas emissions appears to be difficult, actions at the local and state level are gaining prominence as avenues to combat climate change. Cities constitute a key element in this effort, particularly because most of the world population now lives in the main urban centers. All four cities have quite complete climate change plans, although the emphasis they put on different areas varies. By reviewing and comparing these plans, this report aims to highlight some of the best practices and the most innovative actions that major North American cities are undertaking to reduce greenhouse gas emissions. Este trabajo compara los planes de cambio climático de tres megaciudades de América de Norte: México, Los Ángeles y Nueva York, más Toronto. En tanto que la aceptación de un acuerdo global vinculante para reducir las emisiones de gases efecto invernadero parece difícil, las acciones a nivel local y estatal están ganando relevancia como avenidas para combatir el cambio climático. Las cuatro ciudades tienen planes bastante completos, aunque ponen énfasis en diferentes áreas. Al revisar y comparar los cuatro planes, este reporte busca subrayar algunas de las mejores prácticas y de las acciones más innovadoras que las ciudades de América del Norte están instrumentando para reducir las emisiones de gases efecto invernadero.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".