Beyond Logging: The Need for Victoria’s State Forests to Contribute to Australia’s 30 × 30 Protection Target
Bibliographic record
Abstract
In recognition of the declining state of biodiversity, the Convention on Biological Diversity’s Kunming–Montreal Global Biodiversity Framework, signed in late 2022, committed countries to the protection of 30% of the Earth’s terrestrial and inland water areas and coastal and marine areas by 2030. Australia has committed to this target at a national level. The majority of public protected areas (e.g., national parks) in Australia are designated and managed by state and territory governments. The state of Victoria in southeastern Australia has a long history of regional assessments of public land to balance conservation (such as the declaration of protected areas), production of natural resources (e.g., timber harvesting, mineral extraction), and recreation, amongst other uses. The decision to phase out native forest timber harvesting on public land in Victoria presents the greatest opportunity in the state’s history to meet its statewide commitments, national commitments, and international targets, by establishing a comprehensive, adequate, and representative protected area system. We critique Victoria’s reliance on non-binding protections, such as Special Protection Zones in state forests over recent decades, and outline the principles and rationale for the expansion of the protected area system in state forests, recognizing that protected areas are part of a broader suite of future land uses for these public forests.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".