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Record W3046769992 · doi:10.1016/j.jglr.2020.07.017

Registration and application of sea lamprey pheromones for sea lamprey control in the United States and Canada

2020· article· en· W3046769992 on OpenAlexvenueaboutno aff
Kim T. Fredricks, Nicholas S. Johnson, Terrance D. Hubert, Mike Siefkes

Bibliographic record

VenueJournal of Great Lakes Research · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersGreat Lakes Fishery Commission
KeywordsLampreyPetromyzonSex pheromoneTributaryPheromoneFisheryBiologyEcologyGeographyZoology

Abstract

fetched live from OpenAlex

Since the identification of 3-trifluoromethyl-4-nitrophenol as a lampricide in the 1950s, control of sea lamprey populations in the Great Lakes has largely relied on lampricides, barriers, and traps. Lampricide treatments target larval lampreys in tributaries of the Great Lakes. The Great Lakes Fishery Commission oversees sea lamprey control efforts and has invested in technologies that may target other life stages to provide a more integrated approach to sea lamprey control. One technology under development is the use of pheromones to alter behavior of spawning adults. Pheromones are considered biopesticides, which are substances made from naturally occurring products, or derived from living organisms, or a microorganism, that controls pests. We provide a review of sea lamprey management that led to the development of pheromone registration. We also describe the process used to register the first vertebrate pheromone, 3-ketopetromyzonal-24-sulfate (3kPZS) in the United States and Canada and its potential uses in sea lamprey control as a supplemental tool to chemical lampricides.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.086
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.290
Teacher spread0.260 · 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 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

Citations11
Published2020
Admission routes2
Has abstractyes

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