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Record W3203253901 · doi:10.22215/etd/2017-11729

The role of fluctuating selection in the maintenance of genetic variation in Lobelia inflata

2017· dissertation· en· W3203253901 on OpenAlexaff
Kristen Côté

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsCarleton University
Fundersnot available
KeywordsBiologyVariation (astronomy)Genetic variationSelection (genetic algorithm)Evolutionary biologyLife history theoryNatural selectionCampanulaceaeMicrosatelliteLife historyEcologyGeneticsGene

Abstract

fetched live from OpenAlex

Understanding the mechanisms that are responsible for maintaining genetic variation continues to be the focus of much research in evolutionary ecology.It has been suggested that the abundant genetic variation found in Lobelia inflata is maintained by fluctuating selection coupled with temporal genotype-environment interaction.I begin by asking whether microsatellite genotypes exhibit variation in key life-history traits including timing of germination, bolting, flowering and maturation.I used a common garden experiment to show that phenotypic variation exists, that this variation occurs in life-history traits, and that this variation has a genetic basis.Next, I looked at how the microsatellite genotypes that differed in life-history traits expressed differential fitness across environments in a "space-fortime" experiment: I grew multiple lineages under varying conditions to simulate differing natural conditions.Results offer tentative support for the hypothesis that fluctuating selection is responsible for maintaining variation in this system.iii

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.005
GPT teacher head0.237
Teacher spread0.231 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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