High-Resolution Urban Vegetation Gross Primary Productivity Simulation via Plant Functional Type Unmixing: A Case Study in Toronto, Canada
Bibliographic record
Abstract
Despite extensive studies on the productivity of vegetation in natural ecosystems outside cities, remote sensing-based research on urban vegetation and its productivity remains limited due to spectral mixing phenomenon resulting from the vegetation’s fragmented distribution across heterogeneous urban landscapes. To assess the contribution of carbon fixation more accurately by urban vegetation, this study quantified its Gross Primary Productivity (GPP) at high spatial resolution in Toronto, Canada. Regression-based urban vegetation fraction unmixing was conducted on Landsat-8 imagery to separate signals reflected by urban vegetation, whose GPP was then modeled with derived physiological parameters and relevant meteorological data. This study demonstrates the feasibility of estimating urban GPP at a spatial resolution of 30 m using readily available input data. The developed algorithms can be further utilized to investigate spatiotemporal patterns of urban GPP and provide valuable information to conservation agencies and governments in tracking and managing carbon revenues and expenditures at a fine scale.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".