Biogenic Fluxes of Carbon Dioxide in and Around the Greater Toronto and Hamilton Area
Bibliographic record
Abstract
Fluxes of carbon dioxide (CO 2 ) to and from vegetation can be significant on a regional scale. It is therefore important to understand biogenic CO 2 fluxes in order to quantify local carbon budgets. However, these fluxes are often difficult to estimate in urban emission studies. This work uses the Solar Induced Fluorescence (SIF) for Modelling Urban biogenic Fluxes (SMUrF) model and the Urban Vegetation Photosynthesis and Respiration Model (UrbanVPRM) to estimate biogenic CO 2 fluxes in and around the Greater Toronto and Hamilton Area, the most populous region in Canada. We have made several modifications to both vegetation models to improve the agreement with eddy-covariance flux towers in the region and improve estimates over urban areas. In our presentation, we will describe these improvements and our application of these modified models. In particular, we investigate biogenic CO 2 fluxes in the Greenbelt of Ontario; a region surrounding the Greater Toronto and Hamilton Area designed to protect the region's croplands and natural landscape from urban sprawl. We find that this region absorbs significant amounts of CO 2 annually and the recently proposed changes to the Greenbelt will result in reduced sequestration by the Greenbelt. We also investigate the amount of CO 2 absorbed by vegetation estimated by SMUrF and UrbanVPRM in the city of Toronto, Canada. Lastly, we compare the results from this study to anthropogenic CO 2 emission inventories. This work will help constrain biogenic fluxes for use in urban emission studies and may help to inform policy makers and city planners on how vegetation in and around the city affects CO 2 concentrations, and thus carbon budgets.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".