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Record W4365504598 · doi:10.1139/cjb-2022-0107

Butterfly blues: population genetic assessment of wild lupine (<i>Lupinus perennis</i> L.) in endangered Karner blue butterfly habitat around central-west Michigan

2023· article· en· W4365504598 on OpenAlexvenueno aff
Charlyn Partridge, Priscilla A. Nyamai, Alexis Hoskins, Syndell R. Parks

Bibliographic record

VenueBotany · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Research and Chemistry
Canadian institutionsnot available
FundersMichigan Space Grant ConsortiumNational Aeronautics and Space Administration
KeywordsBiologyButterflyEndangered speciesEcologyGenetic diversityPopulationInbreedingHabitatPine barrensHabitat fragmentationLupinusBotany

Abstract

fetched live from OpenAlex

Habitat degradation can have significant effects on native species inhabiting natural ecosystems. Within oak barrens and oak–pine barrens ecosystems, there is a complex interspecies interaction between the federally endangered Karner blue butterfly ( Lycaeides melissa samuelis) and its obligate host plant, wild lupine ( Lupinus perennis L.). Recruitment of wild lupine is critical for maintaining butterfly populations; however, this recruitment can be impeded by habitat fragmentation. Reduced recruitment can result in low genetic diversity in isolated populations, limiting its adaptive potential to respond to environmental change. This study was aimed at understanding the genetic diversity and population structure of wild lupine populations throughout central and west Michigan. We identified significant population structure across most of the populations sampled, with only two sites not significantly different from each other. No sites within our study area displayed statistically significant levels of inbreeding. There are also at least two genetic clusters of wild lupine present within our study region, although there is significant overlap among these groups, indicating that genetic differentiation among clusters may be limited.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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