An active adaptive management case study in Ontario boreal mixedwood stands
Bibliographic record
Abstract
Active adaptive management has recently been advocated for efficiently reducing resource management uncertainties, but no documented applications to forestry issues exist in Ontario. This paper reports the experience of a diverse partnership applying active adaptive management to improve techniques for obtaining desired boreal mixedwood structure and composition in northeastern Ontario. Institutional and economic barriers have been more limiting than technical barriers. Building and maintaining the partnership have required considerable effort, and opportunities for conflict were greatest in the assessment and design steps of the adaptive management cycle. The partnership has maintained its progress by promoting flexibility, trust, and consensus-building. This study demonstrates that classical adaptive management can be simplified for application in local management units. A broader application of active adaptive management in Ontario will require senior decision-makers to endorse a strategy that includes staff retraining, admission of management uncertainties, cooperation among management agencies, stability of long-term funding, encouragement of innovation, and regular adjustment of policies and practices. Key words: consensus-building, designed learning, forest management policies, modelling, monitoring, partnerships, response indicators
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".