Central East Slopes Wolf and Elk Study
Bibliographic record
Abstract
These are the telemetry data collected on both wolf and elk individuals as part of the Central East Slopes Wolf & Elk Study in Alberta, Canada. Elk telemetry data description: We captured adult, female elk during t using a net gun from a helicopter. Each animal was fitted with a Lotek GPS2200 collar (Lotek Wireless, Ontario, Canada) that collected locations every 2 h for up to 11 months. All procedures were approved Province of Alberta animal care permit #1432GP and Univ. of Alberta #300401-300601). Wolf telemetry data description: We captured wolves from 13 packs in 2002–2006 (pack sizes: 3–17 wolves) via helicopter net-gunning during winter months and with modified foot-hold traps during summer (University of Alberta Animal Care Protocols No. 391305, 353112, and 411601). We physically restrained wolves captured with helicopter net-gunning, whereas we either physically restrained or chemically immobilized wolves captured in traps. We fitted 13 wolves with Lotek (Lotek Engineering, Newmarket, ON, Canada) 3300Sw store-onboard GPS collars (2002–2005), and 6 wolves with Lotek 4400S remote-downloadable GPS collars (2005–2006); both collar types featured the advanced-scheduler option. We programmed collars to collect locations at 15-minute, 30-minute, 1-, 2-, 4-, or 6-hour intervals during October– April. We downloaded data from model 3300Sw collars upon retrieval via remote-release mechanism, recapture of the wolf, or when wolves were harvested by trappers. We remotely downloaded data from model 4400S collars every 1–2 weeks during aerial telemetry flights. Global Positioning System collars collected 443–6,676 locations per wolf and remained on wolves for 41–180 days. Collars recorded locations on 90% (3300Sw model) and 82% (4400S model) of fix attempts, suggesting the influence of habitat-induced GPS bias was minimal (Frair et al. 2004). Previous trials using Lotek GPS collars in our study system indicate that 95%of locations fall within 114 m of the true position, with a Bessel distribution (s parameter) of 34 m (Hebblewhite 2006)
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".