A modified co-production framework for improved cross-border collaboration in sustainable forest management and conservation of forest bird populations
Bibliographic record
Abstract
The border between Canada and the United States poses jurisdictional challenges when it comes to consistently implementing science-based conservation of forests and their biological communities. Through a partnership with the Sustainable Forestry Initiative, Boreal Avian Modelling Project, and American Bird Conservancy, we developed a co-production framework to conduct research that will inform forest management practices for bird conservation in the cross-border region of Bird Conservation Region 12. Our framework first responds to the needs of resource managers and other perceived stakeholders, while investing in relationship-building for long term trust as a foundation for future partnerships with Indigenous rights holders and landowners. Our central question was: How can sustainably managed forests create and/or maintain high quality breeding habitat to support forest bird populations that are resilient to climate change? Engaging with experts in Canada and the United States, we found that the main driver for addressing our central question was our limitation in connecting bird population responses to specific forest management practices. We describe how experts are contributing avian count and forest inventory data that researchers will use to produce a requested decision-support tool. We continue to engage with land managers to link forest resource inventory data to specific forest management practices and refine the components of products by including more diverse perspectives.
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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 | 0.087 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".