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Applications of Flow Cytometry to Evolutionary and Population Biology

2007· article· en· W2163127801 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnual Review of Ecology Evolution and Systematics · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsUniversity of Guelph
FundersGoddard Space Flight Center
KeywordsBiologyEvolutionary biologyPopulationPopulation biologyIdentification (biology)Sampling (signal processing)Computational biologyCell sizeEcologyData scienceComputer science

Abstract

fetched live from OpenAlex

Flow cytometry, a method of rapidly characterizing optical properties of cells and cell components within individuals, populations, and communities, is advancing research in several areas of ecology, systematics, and evolutionary biology. Measuring the light emitted or scattered from cells or cell components, often in combination with specific stains, allows a multitude of physical and genetic attributes to be evaluated simultaneously and the resulting information to be rapidly processed. As a result, the technique has enabled large-scale comparative analyses of genome-size evolution, taxonomic identification and delineation, and studies of polyploids, reproductive biology, and experimental evolution. It is also being used to characterize the structure and composition of microbial communities. Here, we outline the nature of these contributions, as well as future applications, and provide an online summary of protocols and sampling methods.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.282
Teacher spread0.275 · 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