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Bibliographic record
Abstract
An analysis of trees in Toronto, Ontario, Canada, reveals that this city has about 10.2 million trees with a tree and shrub canopy that covers approximately 26.6 percent of the city. The most common tree species are eastern white-cedar, sugar maple, and Norway maple. The urban forest currently stores an estimated 1.1 million metric tons of carbon valued at CAD$25.0 million. In addition, these trees remove about 46,700 metric tons of carbon per year (CAD$1.1 million per year) and about 1,905 metric tons of air pollution per year (CAD$16.9 million per year). Trees in Toronto are estimated to reduce annual residential energy costs by CAD$9.7 million per year. The compensatory value is estimated at CAD$7.1 billion. Information on the structure and functions of the urban forest can be used to improve and augment support for urban forest management programs and to integrate urban forests within plans to improve environmental quality in the Toronto area. Cover Photo Aerial shot of High Park and southwest end of Toronto. Photo by Urban Forestry,
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.962 | 0.926 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".