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Record W2128811268 · doi:10.2193/2006-238

Are All Global Positioning System Collars Created Equal? Correcting Habitat‐Induced Bias Using Three Brands in the Central Canadian Rockies

2007· article· en· W2128811268 on OpenAlex
Mark Hebblewhite, Melanie S. Percy, Evelyn H. Merrill

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Wildlife Management · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsAlberta Environment and Protected AreasUniversity of Alberta
FundersUniversity of Montana
KeywordsGlobal Positioning SystemHabitatWildlifeChannel (broadcasting)Logistic regressionFisheryGeographyEnvironmental scienceSelection (genetic algorithm)EcologyStatisticsComputer scienceMathematicsTelecommunicationsBiology

Abstract

fetched live from OpenAlex

Abstract: Global positioning system (GPS) collars are changing the face of wildlife research, yet they still possess biases such as habitat‐induced fix‐rate bias, which is a serious concern for habitat selection studies. We studied GPS bias in the Central Canadian Rockies, a critical area for wildlife conservation, to provide a statistical approach to correct GPS habitat bias for habitat selection studies using GPS collars. To model GPS habitat bias we deployed 11 different collars from 3 brands of GPS collars (Advanced Telemetry Systems [ATS], Asanti, MN; LOTEK Engineering Ltd., Newmarket, ON, Canada; and Televilt, Lindesberg, Sweden) in a random‐stratified design at 86 sites across habitat and topographic conditions. We modeled the probability of obtaining a successful location, P FIX , as a function of habitat, topography, and collar brand using mixed‐effects logistic regression in an information theoretic approach. For LOTEK collars, we also investigated the effect of 8 and 12 GPS channels on fix rate. The ATS collars had the highest overall fix rates (97.4%), followed by LOTEK 12 channel (94.5%), LOTEK 8 channel (85.6%), and Televilt (82.3%). Sufficient model selection uncertainty existed to warrant model averaging for logistic regression P FIX models. Collar brand influenced fix rate in all P FIX models: fix rates for ATS and LOTEK 12 channel were not statistically different, whereas LOTEK 8 channel receivers had intermediate fix rates, and Televilt had the lowest. Fix rate was reduced in aspen stands, closed coniferous stands, and sites in narrow mountainous valleys but was higher on upper mountain slopes. Slight discrepancies between fix rates from field trials and observed species fix rates (wolf [ Canis lupus ] and elk [ Cervus elaphus ]) suggest uncorrected behavioral or movement‐induced bias similar to other recent studies. Regardless, the strong habitat‐induced bias in GPS fix rates confirms that in our study area habitat effects are critical, especially for poorer performance brands. Based on previous studies of effects of the amount of bias on inferences, our results suggest correction for GPS bias should be mandatory for Televilt collars in the Canadian Rockies, optional for LOTEK (dependent on the no. of channels), and unnecessary for ATS. Thus, our GPS bias model will be useful to researchers using GPS collars on a variety of species throughout the Rocky Mountain cordillera.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.259
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it