Remote Sensing of Snowscapes and Caribou (Rangifer tarandus) Movement in the Northwest Territories of Canada
Bibliographic record
Abstract
Recent studies probe that snow characteristics may be primary drivers of migration, largely due to caribou's high level of mobility and their dependence on landscape conditions for locomotion. To investigate whether and how snow characteristics such as melt/refreeze status and the presence of ice are related to caribou movement, we used GPS (Global Positioning System) tracking collar data provided by the Government of the Northwest Territories' Department of Environment and Natural Resources to identify individual animal location and migration patterns, with a focus on the Bathurst herd. We analyzed 117 individual female caribou with more than 30,000 observations between 2007 and 2016 from the Bathurst herd in the Northwest Territories of Canada. We used a hierarchical model to estimate the beginning, duration, and end of spring migration and compared these statistics against snowpack characteristics (i.e., the timing of melt onset and melt/refreeze cycles) which we derived from37 GHz vertically polarized (37V GHz) Calibrated, Enhanced-resolution Brightness Temperatures (CETB) at 3.125 km resolution. We found that the start of spring migration is closely associated with the timing of melt onset and is most often preceded by snow melt onset by just a few days. Melt onset and the start of migration proved very closely associated when plotted across all years, suggesting that melt onset events provide either triggers for migration or favorable conditions that increase mobility. A causal relationship between snowmelt timing and caribou migration would allow for anticipation of caribou migratory behavior and potential shifts in herd ranges.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".