The effect of temporal sampling regime on the characterization of home range for female boreal woodland caribou (Rangifer tarandus caribou) in Labrador, Canada
Bibliographic record
Abstract
Our objective was to determine the influence of temporal sampling regime on the characteristics of individual female caribou home ranges and to explore implications of these findings to the conservation of caribou. The study population was 24 adult female caribou monitored for between 4 and 11 consecutive years between 1986 and 2009 from the Red Wine Mountain (RWM) and Lac Joseph (LJ) herds of boreal caribou in Labrador. We evaluated the influence of length of the monitoring period on the size of home ranges and fidelity of caribou to their ranges by measuring the percent overlap of multi-annual ranges on the total time period a caribou was collared and by calculating displacement between centroids of annual and multi-annual ranges for a given caribou. We found that the size of the range increased with each additional year of monitoring—initially at a rate greater than 20% per year, and then more slowly until an asymptote was reached after 7 years. The distance ratio declined with an increase in the monitoring interval until after approximately 6 years of monitoring. Finally, we evaluated trade-offs between monitoring interval and sample size by measuring the proportion of the total herd range captured by multi-annual ranges for given monitoring interval and sample size combinations. Caribou with the longest monitoring interval inevitably captured the greatest portion of the range at each given sample size. Only monitoring intervals of 4 years or greater captured more than 65% of the herd range even when sample size was doubled for shorter monitoring intervals. Our results suggest that long term monitoring is important when defining the extent of caribou ranges.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".