A synthesis of evidence for the effects of interventions to conserve peatland vegetation: overview and critical discussion
Bibliographic record
Abstract
Peatlands are valuable but threatened ecosystems. Intervention to tackle direct threats is often necessary, but should be informed by scientific evidence to ensure it is effective and efficient. Here we discuss a recent synthesis of evidence for the effects of interventions to conserve peatland vegetation - a fundamental component of healthy, functioning peatland ecosystems. The synthesis is unique in its broad scope (global evidence for a comprehensive list of 125 interventions) and practitioner-focused outputs (short narrative summaries in plain English, integrated into a searchable online database). Systematic literature searches, supplemented by recommendations from an international advisory board, identified 162 publications containing 296 distinct tests of 66 of the interventions. Most of the articles studied open bogs or fens in Europe or North America. Only 36 interventions were supported by sufficient evidence to assess their overall effectiveness. Most of these interventions (85 %) had positive effects, overall, on peatland vegetation - although this figure is likely to have been inflated by publication bias. We discuss how to use the synthesis, critically, to inform conservation decisions. Reflecting on the content of the synthesis we make suggestions for the future of peatland conservation, from monitoring over appropriate timeframes to routinely publishing results to build up the evidence base.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.109 | 0.368 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.028 | 0.016 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".