Uncontrolled field performance of Televilt GPS-Simplex™ collars on grizzly bears in western and northern Canada
Bibliographic record
Abstract
Commercially available telemetry collars for wildlife that employ Global Positioning System (GPS) devices generally have the ability to gather a large volume of precise location data. We appraised the performance of 12-channel Televilt GPS-SimplexTM collars (Televilt/TVP Positioning AB, Lindesberg, Sweden) deployed across western and northern Canada on grizzly bears (Ursus arctos). Of 71 collar deployments between 2000 and 2002, 38 were retrieved and performed according to their programmed schedule, 20 were retrieved and had some degree of failure, and 13 experienced catastrophic failures and were not retrieved. In addition to these deployments, 10 collars failed predeployment. GPS collar fix success rates were greater for the retrieved collars from the Northwest Territories than for the 4 study areas in British Columbia and Alberta (F4, 50 = 10.82, P < 0.001); thus, the latter areas were grouped for further analyses. Collar fix success rates in the British Columbia and Alberta study areas differed between the retrieved collars that functioned normally (x̄ = 65%, SE = 2.3, n = 28) and collars retrieved with failure events (x̄ = 56%, SE = 4.3, n = 17; t43 = 2.09, P = 0.043). Fix success rates were lower the longer collars were in the field (rs = −0.35, n = 45, P = 0.020). Locations from the GPS collars had a mean dilution of precision of <4 for 2D and 3D locations and thus had a good degree of precision. We were satisfied with the volume and quality of the location data; however, we advise other researchers that significant time and money may be lost troubleshooting problems with the Televilt Simplex system. Other recommendations for future and current users are considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".