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Record W2414670758 · doi:10.14430/arctic4561

Utility of Pop-Up Satellite Archival Tags to Study the Summer Dispersal and Habitat Occupancy of Dolly Varden in Arctic Alaska

2016· article· en· W2414670758 on OpenAlexvenueno aff
Michael B. Courtney, Brendan S. Scanlon, Audun H. Rikardsen, Andrew C. Seitz

Bibliographic record

VenueARCTIC · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersDivision of Ocean SciencesAlaska Department of Fish and GameMassachusetts Department of Fish and Game
KeywordsBiological dispersalSalvelinusHabitatFisheryArcticGeographyEcologyOccupancySubmarine pipelineFish <Actinopterygii>OceanographyBiologyGeology

Abstract

fetched live from OpenAlex

In Arctic Alaska, Dolly Varden Salvelinus malma is highly valued as a subsistence fish; however, little is known about its marine ecology. New advances in electronic tagging, such as pop-up satellite archival tags (PSATs), provide scientists with a fishery-independent means of studying several aspects of this species’ movement and ecology. To evaluate the usefulness of this technology, we attached 52 PSATs to Dolly Varden in the Wulik River, which flows from northwestern Alaska into the Chukchi Sea, to study several characteristics of the marine habits of this species. Overall, PSATs provided unprecedented information about summer dispersal of Dolly Varden, including the first evidence of offshore dispersal in the Chukchi Sea, as well as previously documented dispersal types such as movement to other rivers and southerly nearshore movements in northwestern Alaska. On the basis of minimal observable evidence of tag-induced behavioral effects, as well as movements of more than 450 km by fish at liberty (i.e., between tag deployment and release or recapture), we conclude that PSATs offer an effective alternative method for studying several aspects of Dolly Varden dispersal and ecology in areas where it is not practical or feasible to capture these fish, such as coastal and offshore regions of Arctic Alaska

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 score0.017
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.017
GPT teacher head0.251
Teacher spread0.234 · 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

Citations19
Published2016
Admission routes1
Has abstractyes

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Same venueARCTICSame topicFish Ecology and Management StudiesFrench-language works237,207