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Record W2468489547 · doi:10.1038/srep28430

A portable trap with electric lead catches up to 75% of an invasive fish species

2016· article· en· W2468489547 on OpenAlexaff
Nicholas S. Johnson, Scott Miehls, Lisa O’Connor, Gale Bravener, Jessica Barber, Henry T. Thompson, John A. Tix, Tyler Bruning

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersGreat Lakes Fishery Commission
KeywordsTrap (plumbing)Fish <Actinopterygii>FisheryElectric fishEnvironmental scienceBiologyZoology

Abstract

fetched live from OpenAlex

A novel system combining a trap and pulsed direct current electricity was able to catch up to 75% of tagged invasive sea lamprey Petromyzon marinus in free-flowing streams. Non-target mortality was rare and impacts to non-target migration were minimal; likely because pulsed direct current only needed to be activated at night (7 hours of each day). The system was completely portable and the annual cost of the trapping system was low ($4,800 U.S. dollars). Use of the technology is poised to substantially advance integrated control of sea lamprey, which threaten a fishery valued at 7 billion U.S. dollars annually, and help restore sea lamprey populations in Europe where they are native, but imperiled. The system may be broadly applicable to controlling invasive fishes and restoring valued fishes worldwide, thus having far reaching effects on ecosystems and societies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.012
GPT teacher head0.204
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations35
Published2016
Admission routes1
Has abstractyes

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