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Record W2925010136 · doi:10.4490/algae.2019.34.2.25

Novel approaches for generating and manipulating diploid strains of Chlamydomonas reinhardtii

2019· article· en· W2925010136 on OpenAlexafffund
Thamali Kariyawasam, Sunjoo Joo, Ursula Goodenough, Jae‐Hyeok Lee

Bibliographic record

VenueALGAE · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaKorea Carbon Capture and Sequestration R and D CenterDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsChlamydomonas reinhardtiiPloidyChlamydomonasBiologyGeneticsComplementationGenetic analysisMutantMeiosisMatingMating typeMating of yeastModel organismSterilityGene

Abstract

fetched live from OpenAlex

Genetic study of haploid organisms offers the advantage that mutant phenotypes are directly displayed, but has the disadvantage that strains carrying lethal mutations are not readily maintained.We describe an approach for generating and performing genetic analysis of diploid strains of Chlamydomonas reinhardtii, which is normally haploid.First protocol utilizes self-mating diploid strains that will facilitate the genetic analysis of recessive lethal mutations by offering a convenient way to produce homozygous diploids in a single mating.Second protocol is designed to reduce the chance of contamination and the accumulation of spontaneous mutations for long-term storage of mutant strains.Third protocol for inducing the meiotic program is also included to produce haploid mutant strains following tetraploid genetic analysis.We discuss implication of self-fertile strains for the future of Chlamydomonas research.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.243
Teacher spread0.210 · 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 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

Citations9
Published2019
Admission routes2
Has abstractyes

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Same venueALGAESame topicPhotosynthetic Processes and MechanismsFrench-language works237,207