Modelling urban forest structure and services using the urban forest effects (UFORE) model
Bibliographic record
Abstract
Urban forests offer a variety of services, values and benefits to communities. Urban forest resource values include air quality improvements, carbon storage and sequestration, increased property values, energy savings for homeowners and a variety of environmental services. Effective urban forest management for these services and values requires at the very least an understanding of the structure, composition and state of urban forests. The Urban Forest Effects (UFORE) model uses urban forest inventories or sampling data to quantify urban forest structure. The forest structure data is then used to estimate pollution removal, carbon sequestration, pollen allergy ratings, and the effects of shading on building energy use within the urban forest. This paper outlines the UFORE model and its applications for urban forest managers and then discusses a series of recommendations. These include; the incorporation of additional output values in an expansion of the UFORE model; use of new technology to increase accuracy, efficiency and decrease cost of input data collection processes; and potential application of the UFORE model in Canadian cities.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".