MétaCan
Menu
Back to cohort
Record W2402003171 · doi:10.1385/1-59259-741-6:255

Production of Haploid and Diploid Androgenetic Zebrafish

2004· article· en· W2402003171 on OpenAlexafffund
Bruce P. Brandhorst, Graham E. Corley-Smith

Bibliographic record

VenueHumana Press eBooks · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZebrafishBiologyInsertional mutagenesisMutantMutagenesisGeneticsDanioGenetic screenGenePloidyForward geneticsModel organismGenomeComputational biology

Abstract

fetched live from OpenAlex

Zebrafish ( Danio rerio ) are a popular vertebrate model system, particularly useful for research in developmental genetics and neurobiology. The adults are easy to obtain and rear; the generation times are only a few months; and large clutches of big embryos are produced (100–1000 per mating). The embryos develop externally and are nearly transparent, facilitating microscopy, experimental manipulations, and screening for morphological mutants. High-density genetic linkage maps have been produced for visible and DNA markers, and the genome is being sequenced. Several useful genetic tools have been developed. Large-scale mutagenesis screens have detected genes involved in morphogenesis and other developmental processes ( 1 , 2 ). These screens involved a classical three-generation crossing strategy to detect recessive lethal mutations revealed in homozygous diploid mutants. Similar screens are done using insertional mutagenesis to facilitate cloning of the disrupted genes ( 3 ). Many useful molecular markers of differentiating cells have been characterized, and gene expression can be effectively manipulated by the use of morpholino antisense oligonucleotides or expression of dominant-negative mutant proteins. Much information concerning zebrafish mutants, strains, and methods can be found at http://www.zfin.org . These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.006

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 head0.027
GPT teacher head0.285
Teacher spread0.258 · 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

Citations8
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueHumana Press eBooksSame topicZebrafish Biomedical Research ApplicationsFrench-language works237,207