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Record W3208526486 · doi:10.1016/j.jglr.2021.10.015

Exploiting the physiology of lampreys to refine methods of control and conservation

2021· article· en· W3208526486 on OpenAlexaffvenue
Brittney G. Borowiec, Margaret F. Docker, Nicholas S. Johnson, Mary L. Moser, Barbara S. Zielinski, Michael P. Wilkie

Bibliographic record

VenueJournal of Great Lakes Research · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of WindsorUniversity of ManitobaWilfrid Laurier University
FundersGovernment of South AustraliaGreat Lakes Fishery Commission
KeywordsLampreyExtant taxonBiologyEcologyZoologyEvolutionary biologyFishery

Abstract

fetched live from OpenAlex

Lampreys (order: Petromyzontiformes) represent one of two extant groups of jawless fishes, also called cyclostomes. Lampreys have a variety of unique features that distinguish them from other fishes. Here we review the physiological features of lampreys that have contributed to their evolutionary and ecological success. The term physiology is used broadly to also include traits involving multiple levels of biological organization, like swimming performance, that have a strong but not exclusively physiological basis. We also provide examples of how sea lamprey traits are currently being used or investigated to control invasive populations in the Great Lakes, such as reduced capacity to detoxify lampricides, inability to surmount low barriers or dams, and sensitivity to several lamprey-specific chemosensory pheromones and alarm cues. Specific suggestions are also provided for how an improved knowledge of lamprey physiological traits could be exploited for more effective conservation of native lampreys and lead to the development of next generation sea lamprey control and conservation tools.

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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.076
GPT teacher head0.388
Teacher spread0.312 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

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