Safety Net Ontario: Ontario’s outsized role in the “Global Safety Net” for climate and biodiversity
Bibliographic record
Abstract
Dinerstein et al. present a spatially explicit global framework for protected areas needed to reverse catastrophic biodiversity losses and stabilize climate. The Province of Ontario (Canada) stands out in this “Global Safety Net (GSN)” as a critical jurisdiction for meeting those goals, because of both the large extent of roadless lands and high carbon storage in terrestrial ecosystems. Simultaneously, pressure is increasing to develop unmanaged lands in Ontario, particularly in the Far North, for resource extraction. Here, we extract data from the GSN to identify and calculate the areal extent of target regions present in Ontario and critically review the results in terms of accuracy and implications for conservation. We show that when region-specific data are incorporated, Ontario is even more significant than what is shown in the GSN, especially in terms of carbon stocks in forested and open peatlands. Additionally, the biodiversity metrics used in the GSN only partially capture opportunities for conservation in Ontario, and the officially recognized extent of Indigenous lands vastly underestimates the role of First Nations in conservation. Despite these limitations, our analyses indicate that Ontario plays an outsized role in terms of its potential to impact the trajectories both of biodiversity and climate globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".