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Record W2164936172 · doi:10.1139/f00-110

Field test of a new method for tracking small fishes in shallow rivers using passive integrated transponder (PIT) technology

2000· article· en· W2164936172 on OpenAlexvenueno aff
Àlex Haro, Richard A. Cunjak

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTransponder (aeronautics)SalmoFish measurementMarine engineeringTelemetryRemote sensingPopulationFish <Actinopterygii>FisheryAcousticsEnvironmental scienceComputer scienceTelecommunicationsGeologyEngineeringPhysicsBiology

Abstract

fetched live from OpenAlex

A new method for tracking small fishes in shallow streams based on passive integrated transponder (PIT) technology, using a portable reading unit, was investigated. The device consists of a chest-mounted palmtop computer, a reader, and a 12-V battery enclosed in a backpack, connected to a 60-cm-diameter coil antenna mounted on a 4-m-long pole. The method was field tested with wild Atlantic salmon, Salmo salar, parr using transponders 23.1 mm long and 3.9 mm in diameter surgically implanted in the peritoneal cavity of the fish. Laboratory experiments indicated no posttagging mortality for fish &gt; 84 mm in fork length and no tag loss when sutures were used. In the field, tag detection distance was up to 1 m. While moving the antenna above the stream surface, the operator could locate a fish's position to within a square metre. Experiments indicated that more than 80% of tagged parr, on average, were detected by the reader. The technique is a useful alternative to standard radiotelemetry in small-scale environments because PIT tags can be implanted in smaller-bodied fishes and fine-scale movements of individuals can be studied. It can be applied to address numerous questions in the fields of animal behaviour, habitat use, and population dynamics.

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 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)0.000
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 score0.324
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations210
Published2000
Admission routes1
Has abstractyes

Explore more

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