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Record W4400684836 · doi:10.1139/as-2023-0068

Occurrence of the invasive pink salmon (<i>Oncorhynchus gorbuscha</i>, Walbaum 1792) in Greenland 2020 and 2021 as revealed using citizen science, snorkeling, and environmental DNA metabarcoding of fishes in the Kapisillit River

2024· article· en· W4400684836 on OpenAlexvenueno aff
Julius Nielsen, Rasmus Nygaard, Melissa M. Brandner, Kim Præbel

Bibliographic record

VenueArctic Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsnot available
FundersNunatsinni Ilisimatusarnermik SiunnersuisoqatigiitPinngortitaleriffik
KeywordsOncorhynchusFisheryGeographyBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The occurrence of the invasive pink salmon ( Oncorhynchus gorbuscha, Walbaum 1792) in Greenland was initially described in 2019 using social media to collect data on their presence. In this study we continue data collection through social media and initiate a monitoring program of the Kapisillit River in Southwest Greenland using snorkeling and environmental DNA (eDNA) metabarcoding in 2020 and 2021. The Kapisillit River is the only freshwater system in Greenland, where the red-listed Atlantic salmon ( Salmo salar, Linnaeus 1758) is known to spawn. This genetically unique population of Atlantic salmon has been found to decline, wherefore there is general conservation concern that the occurrence of pink salmon at some point can become an additional stressor to the “Kapisillit salmon”. In 2021, pink salmon were present near all larger populated areas in Greenland and likely more abundant than in 2019. From visual observations and using eDNA, the presence of pink salmon was documented in the Kapisillit River in 2021. From the number of individuals observed combined with the spatial distribution of eDNA detections in the river, we suggest that the pink salmon invasion in the Kapisillit River is at an early stage.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.010
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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