Demography of barren-ground grizzly bears
Bibliographic record
Abstract
Between May 1995 and June 1999, we equipped 81 barren-ground grizzly bears (Ursus arctos) with satellite radio collars within a study area of 235 000 km2, centred 400 km northeast of Yellowknife, Northwest Territories, Canada. We used data from radiotelemetry to estimate survival rates, reproductive parameters, and the finite rate of increase of the population (λ). The annual survival rate of adult females was estimated at 0.979 (95% confidence intervals (CI) = 0.9550.998), while the survival rate of adult males was 0.986 (95% CI = 0.9421.0). The cub survival rate was 0.737 (95% CI = 0.6000.844) and the yearling survival rate was 0.683 (95% CI = 0.5140.821). Cub litter size averaged 2.23 (SE = 0.13, n = 35), while yearling litter size decreased to a mean of 1.86 (SE = 0.12, n = 35). The mean litter size of females with 2-year-olds was 1.85 (SE = 0.15, n = 20). The mean birth interval was 2.8 years (SE = 0.3 years, n = 17). The mean reproductive interval, which is calculated by excluding the loss of whole litters from the sample, was 3.9 years (SE = 0.4 years, n = 9). Mean litter size divided by mean birth interval yielded an annual natality rate of 0.81 cubs per adult female per year. The mean age at first parturition was 8.1 years (SE = 0.5 years, n = 10). We believe the population to be currently stable or slightly increasing (λ = 1.033, 95% CI = 1.0081.064).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".