Planning action on nature: Using spatial planning approaches to implement the Kunming-Montreal Global Biodiversity Framework - Conceptual guidelines
Bibliographic record
Abstract
The aim of this report is to demonstrate how spatial planning approaches can be used to support the implementation of the Kunming-Montreal Global Biodiversity Framework specifically focusing on the marine realm. Practical evidence, derived from case studies, of how spatial planning, such as marine spatial planning or integrated coastal zone management, can contribute to solving issues relevant to the targets is provided. Spatial planning approaches provide a practical framework through which to identify and address the interconnections between different targets, potentially helping to streamline efforts to deliver against the wider global biodiversity framework. The report focusses on the following targets in the Kunming-Montreal Global Biodiversity Framework illustrating those that are directly relevant to spatial planning and those which may have a less obvious link.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".