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Record W3093220084 · doi:10.1007/s11160-020-09611-1

Systematic review and meta-analysis of PIT tagging effects on mortality and growth of juvenile salmonids

2020· article· en· W3093220084 on OpenAlexaff
Knut Wiik Vollset, Robert J. Lennox, Eva B. Thorstad, Samuel Auer, Kerstin Bär, Martin H. Larsen, Shad Mahlum, Joacim Näslund, Henrik Stryhn, Ian R. Dohoo

Bibliographic record

VenueReviews in Fish Biology and Fisheries · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsHealth PEI
FundersNorges Forskningsråd
KeywordsJuvenileBiologySalmoFisheryFish <Actinopterygii>Meta-analysisOncorhynchusChinook windEcologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract A systematic review and meta-analysis was conducted to evaluate the appropriate tag:fish size ratio when tagging juvenile salmonids (genera Oncorhynchus , Salmo , and Salvelinus ). The review yielded 18 publications with 211 control and treatment groups reporting results from laboratory studies on the effects of passive integrated transponder (PIT) tags, plus a small number of additional studies on acoustic transmitters. A meta-analysis of the PIT tagging studies showed significant heterogeneity among studies with respect to mortality. Meta-regression revealed that juvenile salmonid mortality increased curvilinearly with the tag:fish length ratio, indicating that mortality risk is rapidly enhanced as smaller fish or larger tag sizes are used. The tag:fish length ratio effect on daily length or mass gain increased linearly. The results provide an estimate of the effects of the tag:fish length ratio on mortality and growth parameters in juvenile salmonids. Based on this, we suggest that researchers can follow best practices for tagging juvenile salmonids with tags that are not greater than 17.5% of fish total length (TL). This equates a minimum size threshold of 131 mm TL for tagging salmonids with 23-mm PIT tags, and 69 mm TL with 12-mm PIT tags. This information can assist researchers, managers, and welfare agencies striving to use the best possible evidence to make informed decisions regarding fish tagging.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.253
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.051
GPT teacher head0.286
Teacher spread0.235 · 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

Citations84
Published2020
Admission routes1
Has abstractyes

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