Declaring success in Sphagnum peatland restoration: Identifying outcomes from readily measurable vegetation descriptors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it