Science and knowledge needs to support Canada’s implementation of the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
Biodiversity continues to decline in Canada despite significant efforts to halt losses. There is increasing recognition that direct drivers of biodiversity loss, such as resource exploitation and pollution, are perpetuated in part by conflicting goals and values across economic, social, political, and technological sectors, and inequity on many scales. Addressing these issues is necessary to create the transformative change required to reverse biodiversity decline. The 2022 Kunming-Montreal Global Biodiversity Framework (KMGBF) aims to build on the lessons learned from previous multilateral attempts to halt biodiversity loss. The framework is supported by 23 targets, to be achieved by 2030, that fall under the themes of reducing threats to biodiversity, sustainable resource use to meet human needs, and implementing tools and solutions to support biodiversity. We consulted with biodiversity experts from diverse fields and sectors including natural-, social- and Indigenous scientists from academia, non-governmental organizations, and government. Here we present this expert community’s perspectives on key knowledge and science needs that will strengthen Canada’s ability to achieve the KMGBF goals and targets. It outlines 78 target-specific needs that range from specific gaps in biodiversity monitoring to suggested research priorities. Eight key concepts were also identified that will support transformative change to address biodiversity loss including 1) more effectively transforming biodiversity knowledge into action, 2) centering Indigenous Peoples and knowledge systems, 3) broadening the lens through which we understand and approach conservation through social science and humanities research, 4) improved collaboration across and within sectors, 5) conservation planning and management with on-the-ground resource users, 6) improved data management, 7) holistic integration across KMGBF targets, 8) understanding how to scale biodiversity information from local to national scales. In addition, we suggest cross-cutting areas of research that could generate important information 1) evaluating the effectiveness of current biodiversity management actions, 2) developing Indigenous biodiversity initiatives to enable the full participation of Indigenous peoples in biodiversity management, 3) enhancing efforts to include social science research in conservation, and 4) identifying how societies and cultures value natural capital and ecosystem services.
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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 | 0.051 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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".