Conserving climate‐change refugia: Insights from research and practice
Bibliographic record
Abstract
Abstract As the impacts of anthropogenic climate change increase, conservation of climate‐change refugia has become a key strategy for effective environmental stewardship. Over the last 5 years, the field of climate‐change refugia conservation has made exciting advances, shifting from concepts and theory to refugia mapping and implementation. However, few studies have advanced to action on the ground; while 84% of studies identified and mapped refugia, only 4% involved implementing management action. Moreover, taxonomic and geographic gaps remain, with most studies focused on terrestrial plants and vertebrates in Europe and North America. Here, we outline impediments to implementation following the steps of the Climate‐Change Refugia Conservation Cycle. Based on a systematic literature review, we elucidate advances and obstacles with examples from a diversity of systems and sectors from across the world and highlight emerging work bridging the gap between research and implementation.
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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 | 0.045 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".