Bibliographic record
Abstract
COVER PHOTO: Many wildlife species, such as grizzly bears (Ursus arctos horribilus), are “unmarked,” bearing no individually recognizable characteristics, thereby preventing easy application of common spatial mark–recapture or mark–resight methods. However, when a portion of the population is marked, for example, with ear tags or GPS collars, ecologists can apply spatial mark–resight models that borrow information from the marked sample to infer spatial density for the unmarked sample. Whittington et al. (Ecosphere, Volume 16, Issue 4, Article e70246; doi: 10.1002/ecs2.70246) used simulations and empirical approaches to evaluate the efficacy of such generalized spatial mark–resight (gSMR) models applied to the grizzly bear, a cryptic, low-density, large carnivore species of conservation concern throughout its distribution. Simulation models confirmed the reliability of two different formulations of the novel gSMR model. When applied to a population of conservation concern in and adjacent to Banff National Park, Alberta, Canada, the authors found that the grizzly bear population increased slightly over a 12-year period and that reproductive females occurred at much lower densities. Spatial models also revealed that density was the highest within protected areas, in high-quality habitat, and in areas far from paved roads in this busy national park setting. This research demonstrates that the model could be applied to other cryptic, low-density, partially marked animals across the globe. Photo credit: Dan Rafla.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.928 | 0.874 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".