Measuring at all scales: sourcing data for more flexible restoration references
Bibliographic record
Abstract
Restoration has long used the reference concept as a cornerstone in setting targets, designing interventions, and benchmarking success. Following the initial applications of restoration references, however, the definition and broader relevance has been debated. Particularly in an era of directional global change, using historic or even contemporary ecosystem models has been contentious among restoration scientists and practitioners. In response, there have been calls for increasing flexibility in how references are defined and diversifying sources of data used to describe a reference. Previous frameworks suggest reference information can be drawn from sources across two main axes of time and space, covering historic to contemporary sources, and near to far spatial scales. We extend these axes by including future projections of climate and species composition and regional ecological information that is spatially disconnected from defined ecosystem types. Using this new framework, we conducted a review of restoration literature published between 2010 and 2020, extracting the temporal and spatial scales of reference data and classifying reference metrics by data type. The studies overwhelmingly focused on contemporary, ecosystem‐specific references to benchmark a completed project's success. The most commonly reported reference metrics were plant‐based, and contemporary reference data sources were more diverse than historical or future reference data. As global conditions continue to shift, we suggest that restoration projects would benefit by expanding reference site information to include forecasted and spatially diverse data. A greater diversity of data sources can enable higher flexibility and long‐term restoration success in the face of global change.
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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 | 0.140 | 0.298 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.044 | 0.043 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.037 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".