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Record W2046230894 · doi:10.1080/02705060.2003.9664493

Evaluation of a Portable Underwater Video Camera to Study Fish Communities in Two Lake Ontario Tributaries

2003· article· en· W2046230894 on OpenAlexaffabout
T. L. Frezza, Leon M. Carl, Scott M. Reid

Bibliographic record

VenueJournal of Freshwater Ecology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsTrent UniversityMinistry of Natural Resources and Forestry
Fundersnot available
KeywordsElectrofishingUnderwaterEnvironmental scienceSampling (signal processing)TributarySTREAMSQuadratVideo cameraFisheryMark and recaptureFish <Actinopterygii>HabitatHydrology (agriculture)Remote sensingGeographyEcologyGeologyComputer scienceCartographyPopulationBiologyArchaeologyTransectComputer vision

Abstract

fetched live from OpenAlex

ABSTRACT We evaluated the suitability of a portable underwater camera to characterize fish communities and streambed particle sizes in two Lake Ontario tributaries. Two video sampling methods were used—quadrat (a stationary camera with a fixed viewing area) and line sampling (camera walked through the stream). Compared to electrofishing surveys, underwater camera data suggested simpler fish communities (biased towards salmonids) and smaller sized fish. Shallow water depths, turbulence, and obstructions (eg., woody debris) limited our ability to identify fish during some of the video samples. Video sampling was able to detect a decreasing trend of fish abundance as water temperatures declined over the course of the study. There was also a strong agreement between measurements of streambed particle sizes done manually in the field and those done with both video sampling methods. The results suggest that a portable underwater camera system could be used to measure seasonal changes in habitat use by salmonid fish species and certain habitat features in streams and rivers.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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)0.0140.000

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 head0.031
GPT teacher head0.279
Teacher spread0.248 · 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.

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

Citations17
Published2003
Admission routes2
Has abstractyes

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