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Record W2133315481 · doi:10.1139/z03-220

Genome and genomics of schistosomes

2004· article· en· W2133315481 on OpenAlexvenueno aff
Guilherme Oliveira, Nilton Barnabé Rodrigues, Álvaro J. Romanha, Diana Bahia

Bibliographic record

VenueCanadian Journal of Zoology · 2004
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyGenomeGenomicsComputational biologyPopulation genomicsgenomic DNADNA sequencingGeneticsComparative genomicsDNA microarrayPopulationGeneEvolutionary biologyGene expression

Abstract

fetched live from OpenAlex

Schistosomes infect over 200 million people and 600 million are at risk. Genomics and post-genomic studies of schistosomes will contribute greatly to developing new reagents for diagnostic purposes and new vaccines that are of interest to the biotechnology industry. In this review, the most recent advances in these fields as well as new projects and future perspectives will de described. A vast quantity of data is publicly available, including short cDNA and genomic sequences, complete large genomic fragments, and the mitochondrial genomes of three species of the genus Schistosoma. The physical structure of the genome is being studied by physically mapping large genomic fragments and characterizing the highly abundant repetitive DNA elements. Bioinformatic manipulations of the data have already been carried out, mostly dealing with the functional analysis of the genes described. Specific search tools have also been developed. Sequence variability has been used to better understand the phylogeny of the species and for population studies, and new polymorphic genomic markers are currently being developed. The information generated has been used for the development of post-genomic projects. A small microarray detected genes that were differentially expressed between male and female worms. The identification of two-dimensional spots by mass spectrometry has also been demonstrated.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.960

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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 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

Citations37
Published2004
Admission routes1
Has abstractyes

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