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Record W2936660301 · doi:10.1186/s40317-019-0168-4

The Gain Reduction Method for manual tracking of radio-tagged fish in streams

2019· article· en· W2936660301 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnimal Biotelemetry · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of AlbertaParks CanadaCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsParks Canada
KeywordsTracking (education)Position (finance)STREAMSStatisticsTelemetryOrientation (vector space)Computer scienceGeodesyMathematicsTelecommunicationsGeologyGeometry

Abstract

fetched live from OpenAlex

Manual tracking has been used since the 1970s as an effective radio telemetry approach for evaluating habitat use of fish in fluvial systems. Radio tags are often located by continually reducing the gain when approaching the tag along a watercourse to estimate its location, termed here as the ‘Gain Reduction Method’. However, to our knowledge the accuracy of this method has not been empirically evaluated and reported in the literature. Here, the longitudinal and lateral positional errors of radio tags are assessed when applying the Gain Reduction Method in a small stream environment. Longitudinal and lateral positional errors (i.e. the difference between the estimated and actual radio tag location) were evaluated based on the distance from the actual tag position, the gain recorded when estimating the tag position and a number of environmental parameters (i.e. stream depth, velocity, stream width and specific conductivity). The manual tracking trials produced an average lateral positional error of 0.91 m (± 1.4) and a longitudinal positional error of 0.66 m (± 0.87). A larger degree of longitudinal positional error was documented when the gain was higher (t = 2.21, p < 0.05). Larger lateral positional error was recorded when the tag was farther across the stream (t = 2.27, p < 0.01) and due to greater inaccuracy in longitudinal positioning (t = 3.2, p = 0.001). In addition, greater rates of lateral positional error were found when specific conductivity levels were higher (t = 2, p < 0.05). Longitudinal and lateral positional errors were not influenced by stream width (m), depth (m) or velocity (m/s). Although the Gain Reduction Method is commonly used to estimate habitat use of stream fishes, there appears to be a paucity of information in the literature that addresses the accuracy for obtaining fine-scale positioning of tagged fishes. This study is aimed to address this knowledge gap by identifying sources of locational error with the Gain Reduction Method. Overall, habitat variables were deemed to be unlikely to have a significant effect on estimating fish position in small streams. Researchers should be aware that error in the longitudinal direction will translate into larger errors in the lateral position. Further exploration of positional accuracy using this active tracking approach is recommended for larger and deeper fluvial systems.

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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)0.001
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 score0.276
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.271
Teacher spread0.261 · 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