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Record W2091166268 · doi:10.3791/1146

Dissection of <em>Saccharomyces Cerevisiae</em> Asci

2009· article· en· W2091166268 on OpenAlexfundno aff
Audrey Morin, Adrian Moores, Michael Sacher

Bibliographic record

VenueJournal of Visualized Experiments · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchConcordia University
KeywordsPloidySaccharomyces cerevisiaeBiologyMeiosisGeneticsYeastMating of yeastMatingStrain (injury)Mating typeCell biologyGeneAnatomy

Abstract

fetched live from OpenAlex

Yeast is a highly tractable model system that is used to study many different cellular processes. The common laboratory strain Saccharomyces cerevisiae exists in either a haploid or diploid state. The ability to combine alleles from two haploids and the ability to introduce modifications to the genome requires the production and dissection of asci. Asci production from haploid cells begins with the mating of two yeast haploid strains with compatible mating types to produce a diploid strain. This can be accomplished in a number of ways either on solid medium or in liquid. It is advantageous to select for the diploids in medium that selectively promotes their growth compared to either of the haploid strains. The diploids are then allowed to sporulate on nutrient-poor medium to form asci, a bundle of four haploid daughter cells resulting from meiotic reproduction of the diploid. A mixture of vegetative cells and asci is then treated with the enzyme zymolyase to digest away the membrane sac surrounding the ascospores of the asci. Using micromanipulation with a microneedle under a dissection microscope one can pick up individual asci and separate and relocate the four ascopores. Dissected asci are grown for several days and tested for the markers or alleles of interest by replica plating onto appropriate selective media.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.391
Teacher spread0.369 · 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 designBench or experimental
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

Citations8
Published2009
Admission routes1
Has abstractyes

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