Modification and Comparison of Two Urban Vegetation Models Over Southern Ontario, Canada
Bibliographic record
Abstract
Abstract Despite significant emissions of anthropogenic carbon dioxide () in cities, fluxes of to and from urban ecosystems can significantly impact local carbon budgets. In this work, we use the city of Toronto, Canada, as a testbed to compare two urban vegetation models: the Solar‐induced chlorophyll fluorescence for Modeling Urban biogenic Fluxes (SMUrF) model and the Urban Vegetation Photosynthesis and Respiration Model (UrbanVPRM). We make several adjustments to both models to improve their agreement with three eddy‐covariance flux towers in the region surrounding the city, enhance the spatial resolution, and better represent biogenic fluxes in urban areas. Compared to flux tower observations, the net ecosystem exchange estimates improved substantially during the spring and autumn for the updated UrbanVPRM and during spring and summer for the updated SMUrF model. These adjustments also result in significantly better agreement between the two models in Toronto during 2018–2021. While discrepancies remain between the updated models, likely due to the use of different driving variables, they are substantially smaller than differences between anthropogenic emissions estimated by two commonly used emission inventories. We find that during summer afternoons both the UrbanVPRM and SMUrF models predict uptake of between half and all of Toronto's mean anthropogenic summer afternoon emissions, depending on the inventory used. During nights and the non‐growing season, vegetation emits , amounting to between a quarter and half of Toronto's human‐caused emissions during summer nights. This illustrates the significance of biogenic fluxes on the urban budget, especially on hourly timescales.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".