Mapping and assessing the knowledge base of ecological restoration
Bibliographic record
Abstract
Information on restoration science and practice is dispersed across large numbers of scientific papers, reports, books, and other resources, and there is a lack of synthetic approaches and of linkages between ecological theory and practice. With recent calls for scaling up ecological restoration, there is an urgent need for improving the effectiveness of restoration ecology by presenting existing knowledge in an organized and accessible form. Practitioners benefit from knowing which theories explain patterns and processes in a specific ecosystem, and scientists need an overview of empirical evidence supporting current theories. Strengthening links between restoration practice and science benefits both areas. Based on a new approach used for organizing and assessing hypotheses in invasion biology, we suggest the development of an interactive online platform that promotes the integration of restoration science and practice by (1) presenting an overview of restoration ecology; (2) mapping theoretical work relevant for ecological restoration; (3) displaying direct links to relevant publications; and (4) providing summaries of empirical evidence for ecological theories in specific settings. This online knowledge base should be developed in an open process, bringing together the restoration community with experts in semantic web and natural language processing, library scientists, web designers, and other specialists. The platform should become an evolving, searchable, openly accessible, and intuitively organized tool for future ecological restoration.
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 | 0.025 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.065 | 0.039 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".