The Canadian Mountain Network: Advancing Innovative, Solutions-Based Research to Inform Decision-Making
Bibliographic record
Abstract
The Canadian Mountain Network (CMN) launched in 2019 as a new national research network dedicated to the resilience and health of mountain peoples and places. Supported by complementary training, knowledge mobilization, and networking programs, CMN's research program represents a once-in-a-generation opportunity to identify and address mountain knowledge gaps in Canada. Working with Indigenous and Western ways of knowing, the Network will support decision-making and action in Canada and globally to advance sustainable mountain development. A key element of our strategy is the launch of the landmark Canadian Mountain Assessment, which will address 3 fundamental questions: what do we know, not know, and need to know about Canada's diverse and rapidly changing mountain systems? CMN is honored by the opportunity to join the International Mountain Society and looks forward to building new linkages between Canadian and international mountain research communities.
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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 | 0.075 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".