Bibliographic record
Abstract
Abstract Nearly a quarter of Canada’s landmass is covered by mountainous terrain, making mountains an important aspect of the physical and human geography of the country. Mountain areas in Canada have motivated a great deal of research activity, yet the state of mountain research in the country has never been systematically characterized, precluding a detailed understanding of what is being studied, when, where, how, and by whom. In response, we conducted a systematic scoping review to rigorously identify, collate, and critically examine existing peer-reviewed articles related to mountains in Canada. 2,888 articles were included in our review, which reveals strong biases towards work in the natural sciences and in the mountain west, with little work to date in the social and health sciences or in other mountainous regions of the country. Our results demonstrate that Canada is among the most productive contributors to mountain research globally, but that topical and geographical biases in existing research effort leave important gaps that must be addressed to successfully navigate challenges and opportunities facing mountain areas in Canada. We provide a roadmap to guide future mountain-focused research activities in the country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.035 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".