Maintaining the role of Canada’s forests and peatlands in climate regulation
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
Canada’s forest and peatland ecosystems are globally significant carbon stores, whose management will be influenced by climate change mitigation policies such as offset systems. To be effective, these policies must be grounded in objective information on the relationships between land use, ecosystem carbon dynamics, and climate. Here, we present the outcomes of a workshop where forest, peatland, and climate experts were tasked with identifying management actions required to maintain the role of Canada’s forest and peatland ecosystems in climate regulation. Reflecting the desire to maintain the carbon storage roles of these ecosystems, a diverse set of management actions is proposed, incorporating conservation, forest management, and forest products. Key words: forests, peatlands, carbon, Canada, climate change, management, forest products, conservation
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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) | 0.000 | 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 it