Setting goals and priorities for restoration strategies in the context of disparate historical interpretations: An example from the Garry oak and Douglas fir mosaic of Mount Maxwell, Salt Spring Island, British Columbia
Bibliographic record
Abstract
Restoration of landscapes and ecological relationships differs from other kinds of ecosystem management in that goals are based on recreating conditions thought to have existed at a point in history especially appreciated or privileged. Different reconstructions and interpretations of environmental history can generate divergent restoration goals and priorities. Aboriginal communities have had a long presence on Mount Maxwell with its mosaic of Garry oak, Quercus garryana, savannah and woodland and Douglas fir, Pseudotsuga menziesii, forest. Adjacent Burgoyne Bay was the site of one of British Columbia's more traumatic 'Indian wars' in the mid-Nineteenth Century. Later in the nineteenth century, settlers brought sheep and invasive plants Europe. There was active aboriginal land use into the Twentieth Century. In the early part of the Twentieth Century, wildfire and aboriginal burning was suppressed. Without fire, the oak savannahs are becoming dominated by woodlands and Douglas fir forest. But other historical points, necessary for setting defensible and fundable restoration goals increasingly scrutinized by community groups, remain unresolved. On Mount Maxwell, restoration advocacy and activities began twenty-five years ago and have been increasingly related to biodiversity conservation and species at risk. A number of divergent interpretations of the history of the area are presented with corresponding restoration goals and priorities. This discussion is a dialogue between the first author, who completed the first management and restoration plan for Mount Maxwell in 1981, and a former student and new practitioner.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.038 | 0.014 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".