Vertebrate biodiversity on Indigenous-managed lands in Australia, Brazil, and Canada equals that in protected areas
Bibliographic record
Abstract
This is the data repository accompanying the publication 'Vertebrate biodiversity on Indigenous-managed lands in Australia, Brazil, and Canada equals that in protected areas' Abstract: Declines in global biodiversity due to land conversion and habitat loss are driving a ‘Sixth Mass Extinction’ and many countries fall short of meeting even nominal targets for land protection. We explored how such shortfalls in Australia, Brazil and Canada might be addressed by enhancing partnerships between Indigenous communities and other government agencies that recognize and reward the existing contributions of Indigenous-managed lands to global biodiversity conservation and their potential contribution to meeting international treaty targets. We found that Indigenous-managed lands were slightly more species rich than existing protected areas in all three countries, and in Brazil and Canada, that they supported more threatened species than existing protected areas or non-protected areas. Our results suggest that overall, Indigenous-managed lands and existing protected areas host similar levels of biodiversity in Brazil, Canada, and Australia. Partnerships with Indigenous communities that seek to maintain or enhance Indigenous land tenure practices on Indigenous-managed lands may therefore have some potential to ameliorate national and global shortfalls in land protection for biodiversity conservation using a mix of conventional protected areas and Indigenous-managed lands.
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 | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 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) | 0.008 | 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".