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Record W4389781539 · doi:10.1080/02705060.2023.2292232

Ecological niche modeling of diploid flowering rush ( <i>Butomus umbellatus</i> L.) in the United States

2023· article· en· W4389781539 on OpenAlexaboutno aff
Maxwell G. Gebhart, Ryan M. Wersal

Bibliographic record

VenueJournal of Freshwater Ecology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersU.S. Army Corps of EngineersDirectorate for Biological Sciences
KeywordsAbiotic componentEcologyNicheEcological nicheBiologyHabitatRange (aeronautics)PloidyPerennial plantEnvironmental niche modellingGeography

Abstract

fetched live from OpenAlex

Flowering rush (Butomus umbellatus L.) is an invasive perennial monocot found along the United States (U.S.).–Canadian border which can grow into large monotypic mats that can cause water use issues. Currently, there are two known cytotypes, diploid and triploid, within the invaded range; however, basic ecological research is lacking on the diploid cytotype. Ecological niche modelling (ENM) was done on three known populations of the diploid cytotype, alongside a global site model, to determine site-specific abiotic influences and potential suitability within the U.S. The ENM was constructed using climatic and soil variables from public sources with resultant models compared to currently known populations of flowering rush throughout the U.S. Diploid flowering rush populations and the global site models were highly reliant on precipitation in the driest month (27–39% model contribution) and one site was highly reliant on precipitation seasonality (38% model contribution). Diploid flowering rush populations in this study displayed different responses towards abiotic factors; however, seasonal signaling of precipitation patterns are highly important. Furthermore, diploid flowering rush is predicted to invade numerous areas with less than suitable habitat which should warrant further monitoring to prevent further spread.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.034
GPT teacher head0.264
Teacher spread0.230 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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