MétaCan
Menu
Back to cohort
Record W3133478091 · doi:10.3996/jfwm-20-031

A Three-Pass Electrofishing Removal Strategy Is Not Effective for Eradication of Prussian Carp in a North American Stream Network

2020· article· en· W3133478091 on OpenAlexaffabout
Jamie T. Card, Caleb T. Hasler, Jonathan L. W. Ruppert, Caitlyn Donadt, Mark S. Poesch

Bibliographic record

VenueJournal of Fish and Wildlife Management · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of WinnipegUniversity of Alberta
Fundersnot available
KeywordsElectrofishingCarassiusPrussian blueCarpFisheryTributaryInvasive speciesPopulationFreshwater fishBiologyEcologyFish <Actinopterygii>Environmental scienceGeographyChemistryMedicine

Abstract

fetched live from OpenAlex

Abstract Prussian Carp Carassius gibelio, also referred to as Gibel Carp, is a destructive aquatic invasive species, recently found in Alberta, Canada. Three-pass electrofishing is a potential approach to control some aquatic invasive fish species in stream habitats. The objectives of this study were to 1) determine the efficacy of this strategy to control Prussian Carp in connected streams and 2) assess whether population size or the distance to the introduction site would influence removal success. We sampled sites by using electrofishing in tributaries of the Red Deer River in both the summer and fall and detected Prussian Carp at all sites before removal, with &amp;gt;90% probability of detection of this species within the first 120 m of electroshocking efforts. Overall, we were not successful at removing Prussian Carp from the sample sites, and we found that abundances of Prussian Carp were significantly higher postremoval. Removal success related significantly to distance to the introduction site, suggesting that removal may be useful in targeted situations close to the edge of the invasion front.

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 score0.396
Threshold uncertainty score0.405

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 science0.0000.000
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Fish and Wildlife ManagementSame topicFish Ecology and Management StudiesFrench-language works237,207