Ecological Footprint and Biocapacity Accounts for Rural Communities in Ontario
Bibliographic record
Abstract
The purpose of this research partnership, with the Ecological Footprint Institute at York University and the Rural Ontario Institute, was to conduct down-scaled community level Ecological Footprint and Biocapacity accounts (EFBAs) for communities in Ontario. Eight community EFBAs were created as a proof-of-concept design of the accounts, this design was later applied to all communities in Ontario. A top-down methodology was applied, by downscaling Ontario’s Ecological Footprint and Biocapacity and applying data to create scaling metrics. Data sources utilized for the EFBAs, included Statistics Canada, Southern Ontario Land Resource Information System (SOLRIS) 3.0, and Ontario Land Cover Compilation (OLCC) v.2.0. The Statistics Canada data was applied to the Ontario Consumption Land-Use Matrix (CLUM) to scale down the Ontario CLUM data to the community level. SOLRIS and OLCC data was used to identify the area for various land classifications and the Ontario Ecological Footprint report was used to match biocapacity classifications to these land classifications. The resulting EFBAs provided environmental data that will be used by municipal stakeholders to inform decision-making on achieving climate and net-zero goals. This project represents the first-time EFBAs have been created for rural Ontario and presents new data and methodologies to expand the planetary accounting field.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".