Canada’s human footprint reveals large intact areas juxtaposed against areas under immense anthropogenic pressure
Bibliographic record
Abstract
Efforts are underway in Canada to set aside terrestrial lands for conservation, thereby protecting them from anthropogenic pressures. Here we produce the first Canadian human footprint map by combining 12 different anthropogenic pressures and identifying intact and modified lands and ecosystems across the country. Our results showed strong spatial variation in pressures across the country, with just 18% of Canada experiencing measurable human pressure. However, some ecosystems are experiencing very high pressure, such as the Great Lakes Plains and Prairies national ecological areas that have over 75% and 56% of their areas, respectively, with a high human footprint. In contrast, the Arctic and Northern Mountains have less than 0.02% and 0.2%, respectively, of their extent under high human footprint. A validation of the final map, using random statistical sampling, resulted in a Cohen Kappa statistic of 0.91, signifying an “almost perfect” agreement between the human footprint and the validation data set. By increasing the number and accuracy of mapped pressures, our map demonstrates much more widespread pressures in Canada than were indicated by previous global mapping efforts, demonstrating the value in specific national data applications. Ecological areas with immense anthropogenic pressure highlight challenges that may arise when planning for ecologically representative protected areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".