The Temporal and Spatial Distribution of Carbon Dioxide Emissions from Fossil-Fuel Use in North America
Bibliographic record
Abstract
Abstract Refinements in the spatial and temporal resolution of North American fossil-fuel carbon dioxide (CO2) emissions provide additional information about anthropogenic aspects of the carbon cycle. In North America, the seasonal and spatial patterns are a distinctive component to characterizing anthropogenic carbon emissions. The pattern of fossil-fuel-based CO2 emissions on a monthly scale has greater temporal and spatial variability than the flux aggregated to the national annual level. For some areas, monthly emissions can vary by as much as 85% for some fuels when compared with monthly estimates based on a uniform temporal and spatial distribution. The United States accounts for the majority of North American fossil carbon emissions, and the amplitude of the seasonal flux in emissions in the United States is greater than the total mean monthly emissions in both Canada and Mexico. Nevertheless, Canada and Mexico have distinctive seasonal patterns as well. For the continent, emissions were aggregated on a 5° × 10° latitude–longitude grid. The monthly pattern of emissions varies on both a north–south and east–west gradient and evolves through the time period analyzed (1990–2007). For many areas in North America, the magnitude of the month-to-month variation is larger than the total annual emissions from land use change, making the characterization of emissions patterns essential to understanding humanity’s influence on the carbon cycle.
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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 | 0.002 |
| 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) | 0.001 | 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".