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Record W3009478773 · doi:10.1139/juvs-2018-0039

Measurements of juvenile Atlantic bluefin tuna (<i>Thunnus thynnus</i>) size using an unmanned aerial system

2020· article· en· W3009478773 on OpenAlex
J. Michael Jech, Molly E. Lutcavage, Angelia S. M. Vanderlaan, Yuri Rzhanov, Don LeRoi

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Unmanned Vehicle Systems · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersNational Marine Fisheries ServiceNational Oceanic and Atmospheric AdministrationUniversity of Massachusetts BostonUniversity of Massachusetts
KeywordsThunnusTunaAltimeterRemote sensingCalibrationAerial surveyGlobal Positioning SystemFish <Actinopterygii>TakeoffEnvironmental scienceFisheryGeologyComputer scienceBiologyEngineeringMathematics

Abstract

fetched live from OpenAlex

An APH-22 vertical-takeoff-and-landing hexacopter was used to collect aerial images of schools and individuals of juvenile Atlantic bluefin tuna (ABFT; Thunnus thynnus) at the sea surface in the southern Gulf of Maine. Quantitative measures of fish length, width, and inter-fish spacing were obtained from these images by applying calibration settings and performance measures from calibrating, testing, and evaluating the onboard motion and altimeter sensors and the digital camera and lenses. The accuracy and precision of the onboard motion sensors, camera, and lens calibrations were sufficient to provide length measurements to sub-centimeter precision, but the altimeter performance was least reliable and required additional information, such as images of known-sized objects during each flight, to provide measurements at the accuracy and precision needed for data to be incorporated in fisheries management. The APH-22 was ideal for acquiring images of ABFT individuals and schools and may be a useful tool for remotely monitoring the behavior and body condition of these elusive animals.

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)0.001
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 score0.105
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.040
GPT teacher head0.238
Teacher spread0.198 · 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