Knowledge sharing for shared success in the decade on ecosystem restoration
Bibliographic record
Abstract
Abstract The Decade on Ecosystem Restoration aims to provide the means and incentives for upscaling restoration efforts worldwide. Although ecosystem restoration is a broad, interdisciplinary concept, effective ecological restoration requires sound ecological knowledge to successfully restore biodiversity and ecosystem services in degraded landscapes. We emphasize the critical role of knowledge and data sharing to inform synthesis for the most robust restoration science possible. Such synthesis is critical for helping restoration ecologists better understand how context affects restoration outcomes, and to increase predictive capacity of restoration actions. This predictive capacity can help to provide better information for evidence‐based decision‐making, and scale‐up approaches to meet ambitious targets for restoration. We advocate for a concerted effort to collate species‐level, fine‐scale, ecological community data from restoration studies across a wide range of environmental and ecological gradients. Well‐articulated associated metadata relevant to experience and social or landscape contexts can further be used to explain outcomes. These data could be carefully curated and made openly available to the restoration community to help to maximize evidence‐based knowledge sharing, enable flexible re‐use of existing data and support predictive capacity in ecological community responses to restoration actions. We detail how integrated data, analysis and knowledge sharing via synthesis can support shared success in restoration ecology by identifying successful and unsuccessful outcomes across diverse systems and scales. We also discuss potential interdisciplinary solutions and approaches to overcome challenges associated with bringing together subfields of restoration practice. Sharing this knowledge and data openly can directly inform actions and help to improve outcomes for the Decade on Ecosystem Restoration.
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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 | 0.131 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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".