NESP RL Project 3.17 - Improving environmental outcomes on conserved and managed lands
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework (GBF) of the UN Convention on Biological Diversity (CBD) outlines key goals and targets that specifically acknowledge the importance of protecting, conserving and managing land and water effectively. While protected areas are well understood and studied, conserved and managed lands and their role in delivering environmental and social outcomes are not. To this end, ‘other effective area-based conservation measures’ (OECMs; termed ‘conserved areas’ in Target 3 of the GBF) will contribute alongside protected areas to meet area-based measures of 30% of land and sea by 2030. Similarly, managed areas which surround and connect protected and conserved areas are essential for meeting the broader goal that the integrity, connectivity and resilience of ecosystems are maintained and enhanced. Protected, conserved and managed areas will necessarily work in tandem to improve landscape-scale habitat quality and connectivity and, ultimately, overall persistence of biodiversity. This project will review private-land conservation programs against the draft Australian OECM framework criteria to indicate the types of programs that are likely to qualify. Based on this review, programs that are near OECM standard but that fall short on key criteria, such as actively managing for biodiversity outcomes, will be considered for place-based research. Within programs, the research will design and deploy interventions to support landholders to manage their land consistent with OECM criteria and track the outcomes of this management over time to demonstrate enduring biodiversity outcomes. The research will demonstrate if the hypothesised benefits to both biodiversity and participants of these programs (e.g. increased nature connectedness) hold true. If our findings are positive, we expect these interventions could be added to private-land conservation programs in other jurisdictions.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".