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Record W2043824913 · doi:10.1139/cjb-2014-0135

A PCR-RFLP method to detect hybridization between the invasive Eurasian watermilfoil (<i>Myriophyllum spicatum</i>) and the native northern watermilfoil (<i>Myriophyllum sibiricum</i>), and its application in Ontario lakes

2014· article· en· W2043824913 on OpenAlexafffundvenueabout
Simon F. Grafe, Céline Boutin, Frances R. Pick, Roger D. Bull

Bibliographic record

VenueBotany · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsCanadian Museum of NatureCarleton UniversityEnvironment and Climate Change Canada
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsMyriophyllumBiologyRestriction fragment length polymorphismBotanyInvasive speciesEcologyPolymerase chain reactionAquatic plantGeneGenetics

Abstract

fetched live from OpenAlex

The discovery of hybridization between the invasive Eurasian watermilfoil (Myriophyllum spicatum L.) and native northern watermilfoil (Myriophyllum sibiricum Kom.) has generated interest in establishing the hybrid’s distribution and invasiveness. Identification of hybrid M. spicatum × M. sibiricum requires molecular genetic analysis, however, as the hybrid’s morphology overlaps with both parent species. Using plants collected from 10 lakes in Ontario, Canada, we compared a previous method of identification (sequencing the nuclear ITS region) with a simpler screening method (PCR-RFLP of the ITS region). Both methods agreed on the identification of hybrid M. spicatum × M. sibiricum and both parent species, supporting the suitability of PCR-RFLP to screen for the hybrid. Four of 29 samples were identified as hybrid M. spicatum × M. sibiricum, which were all found in three adjacent lakes associated with the Rideau Canal Waterway. The PCR-RFLP method should enable greater sampling effort to screen for hybrid M. spicatum × M. sibiricum and establish its geographic distribution across connected waterways.

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)0.001
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 score0.162
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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

Citations11
Published2014
Admission routes4
Has abstractyes

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