MétaCan
Menu
Back to cohort
Record W2094505819 · doi:10.2741/1287

Extracellular virulence factors of streptococci associated with animal diseases

2004· review· en· W2094505819 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in bioscience · 2004
Typereview
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsCegep de Saint Hyacinthe
FundersUniversité de Montréal
KeywordsVirulenceMicrobiologyHemolysinVirulence factorProteasesBiologyExtracellularImmune systemBacteriaStreptococcus pyogenesEnzymeImmunologyStaphylococcus aureusGeneGenetics

Abstract

fetched live from OpenAlex

A virulence factor denotes a bacterial product or strategy that contributes to virulence or pathogenicity. Streptococci produce a variety of protein toxins and enzymes that are capable of killing host cells and breaking down cell constituents, presumably to provide nutrients for the bacteria or to promote their spread. Some of these secreted products are hemolysins, streptokinases, hyaluronidases, exotoxins and proteases. In some cases, they play an important role in resistance to the host immune system, acting alone or in combination with cell-associated virulence factors (such as the capsule and surface proteins). Thus, the virulence of streptococci is considered as a multifactorial process. In contrast to well known human pathogens, and in spite of their veterinary importance, knowledge of virulence factors of most animal disease-associated streptococci is limited or almost inexistent. In the present article, the available information regarding the extracellular virulence factors of the most important animal disease-related streptococci is reviewed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.351
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.034
GPT teacher head0.315
Teacher spread0.281 · 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