Declaring success in Sphagnum peatland restoration: Identifying outcomes from readily measurable vegetation descriptors
Bibliographic record
Abstract
Managers of restoration projects need readily applicable tools that give them an unequivocal declaration of success or failure based on primary goals that may vary according to different jurisdictions. We used restored extracted Sphagnum peatlands in Canada to illustrate how different types of plant communities assigned to different restoration outcomes can be identified from readily measurable descriptors. Vegetation was surveyed from 5–10 years after restoration at 2–3 year intervals in a total of 274 permanent plots in 66 restored peatlands located across 4500 km, from Alberta in the drier continental interior to the wetter maritime coastal province of New Brunswick. Plant community data were subjected to a k-means clustering that resulted in three restoration outcome categories. A linear discriminant analysis (LDA) model (the “declaration tool”) correctly classified 91 % of the plots in a calibration database that included 75 % of the peatlands, and 93 % of the validation database (25 % of the peatlands), into the restoration outcome categories, using plant strata and number of years since restoration (only) as descriptors. The model includes classification functions that can be used to assign a new plot (not used to construct the model) to its restoration outcome category. We found that ~70 % of the severely degraded peatland is successfully regenerating towards the target plant community.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".