Urban ecosystems and the North American carbon cycle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Approximately 75–80% of the population of North America currently lives in urban areas as defined by national census bureaus, and urbanization is continuing to increase. Future trajectories of fossil fuel emissions are associated with a high degree of uncertainty; however, if the activities of urban residents and the rate of urban land conversion can be captured in urban systems models, plausible emissions scenarios from major cities may be generated. Integrated land use and transportation models that simulate energy use and traffic‐related emissions are already in place in many North American cities. To these can be added a growing dataset of carbon gains and losses in vegetation and soils following urbanization, and a number of methods of validating urban carbon balance modeling, including top down atmospheric monitoring and urban ‘metabolic’ studies of whole ecosystem mass and energy flow. Here, we review the state of our understanding of urban areas as whole ecosystems with regard to carbon balance, including both drivers of fossil fuel emissions and carbon cycling in urban plants and soils. Interdisciplinary, whole‐ecosystem studies of the socioeconomic and biophysical factors that influence urban carbon cycles in a range of cities may greatly contribute to improving scenarios of future carbon balance at both continental and global scales.
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.
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 it