Ottawa's urban forest : a geospatial approach to data collection for the UFORE/i-Tree eco ecosystem services valuation model
Bibliographic record
Abstract
The i-Tree Eco model, developed by the U.S. Forest Service, is commonly used to estimate the value of the urban forest and the ecosystem services trees provide. The model relies on field-based measurements to estimate ecosystem service values. However, the methods for collecting the field data required for the model can be extensive and costly for large areas, and data collection can thus be a barrier to implementing the model for many cities. This study investigated the use of geospatial technologies as a means to collect urban forest structure measurements within the City of Ottawa, Ontario. Results show that geospatial data collection methods can serve as a proxy for urban forest structure parameters required by i-Tree Eco. Valuations using the geospatial approach are shown to be less accurate than those developed from field-based data, but significantly less expensive. Planners must weigh the limitations of either approach when planning assessment projects.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".