Need and opportunity for a North American caribou knowledge cooperative
Bibliographic record
Abstract
The importance of migratory caribou (Rangifer tarandus) to northerners, the increasing pressure to extract non-living resources, and predicted global climate change have led researchers, managers and resource users alike to focus on how to improve our knowledge of this unique northern ungulate. Unprecedented threats to caribou sustainability, along with the increasingly acknowledged value of indigenous hunters’contribution to caribou research, pose the additional challenge to innovate research methods that accommodate differing cultural perspectives and facilitate communication among groups. This paper surveys the state of scientific knowledge of the eleven major northern mainland herds of North America. We recommend an approach to improve our working knowledge of barren-ground caribou in order to assess better future impacts. The transfer of knowledge gained from years of research and indigenous experience on many aspects of caribou ecology should be evaluated and, where applicable, transferred to herds with more modest databases. The establishment of a North American Caribou Monitoring and Assessment System, based on a synthesis of local knowledge and research-based science, is recommended as a tool for improved communication and collective learning.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".