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Record W6967106567 · doi:10.5061/dryad.7kp78

Data from: Evolutionary stasis despite selection on a heritable trait in an invasive zooplankton

2015· dataset· en· W6967106567 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2015
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSelection (genetic algorithm)Natural selectionTraitSPINE (molecular biology)Stabilizing selectionZooplanktonAdaptation (eye)Evolutionary ecology

Abstract

fetched live from OpenAlex

Invasive species are one of the greatest threats to ecosystems and there is evidence that evolution plays an important role in the success or failure of invasions. Yet, few studies have measured natural selection and evolutionary responses to selection in invasive species, particularly invasive animals. We quantified the strength of natural selection on the defensive morphology (distal spine) of an invasive zooplankton, Bythotrephes longimanus, in Lake Michigan across multiple months during three growing seasons. We used multiple lines of evidence, including historic and contemporary wild-captured individuals and paleoecology of retrieved spines, to assess phenotypic change in distal spine length since invasion. We found evidence of temporally variable selection, with selection for decreased distal spine length early in the growing season and selection for increased distal spine length later in the season. This trend in natural selection is consistent with seasonal changes in the relative strength of non-gape-limited and gape-limited fish predation. Yet, despite net selection for increased distal spine length and a known genetic basis for distal spine length, we observed little evidence of an evolutionary response to selection. Multiple factors likely limit an evolutionary response to selection, including genetic correlations, trade-offs between components of fitness, and phenotypic plasticity.

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)0.002
metaresearch head score (Gemma)0.008
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.050
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0500.038

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 head0.078
GPT teacher head0.311
Teacher spread0.232 · 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
GenreDataset

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
Published2015
Admission routes1
Has abstractyes

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