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Record W2935872018 · doi:10.22621/cfn.v132i3.1943

First record of Eurasian Water-milfoil, <i>Myriophyllum spicatum</i>, for the Saint John River, New Brunswick

2019· article· en· W2935872018 on OpenAlexaffvenueabout
Meghann Bruce, Tommi Linnansaari, R. Allen Curry

Bibliographic record

VenueThe Canadian Field-Naturalist · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMyriophyllumHabitatContext (archaeology)EcologyAquatic plantSAINTIntroduced speciesInvasive speciesMacrophyteBiologyGeographyArchaeologyHistory

Abstract

fetched live from OpenAlex

Eurasian Water-milfoil (Myriophyllum spicatum L.) is regarded by conservation practitioners as one of the most challenging invasive aquatic plants to manage. Owing to its broad tolerance to environmental conditions, vegetative propagation, and rapid establishment and growth, M. spicatum introductions have the potential to drastically alter macrophyte species assemblages via a loss of native species and their respective ecosystem functions. Following the discovery of a single specimen of M. spicatum in the Saint John River, near Fredericton, New Brunswick (Canada) we further investigated the localized distribution of this nonindigenous species. Thirteen areas were identified as potential M. spicatum habitat and were surveyed by wading or snorkeling. Specimens of M. spicatum were collected and morphological identifications were verified through genetic analyses (ITS2; rbcLa). The results of our investigation confirm the presence of M. spicatum at six different locations within the Saint John River. Here we discuss the implications of this discovery in the context of the contiguous aquatic habitats along a large river system.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.558

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.198
Teacher spread0.190 · 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

Citations2
Published2019
Admission routes3
Has abstractyes

Explore more

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