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The ABCs and 123s of Bacterial Secretion Systems in Plant Pathogenesis

2014· review· en· W2113257791 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.

Bibliographic record

VenueAnnual Review of Phytopathology · 2014
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogenic Bacteria Studies
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical Sciences
KeywordsSecretionBiologyCell biologyPathogenicityExtracellularBacteriaMicrobiologyCell envelopePathogenic bacteriaBiochemistryEscherichia coliGeneticsGene

Abstract

fetched live from OpenAlex

Bacteria have many export and secretion systems that translocate cargo into and across biological membranes. Seven secretion systems contribute to pathogenicity by translocating proteinaceous cargos that can be released into the extracellular milieu or directly into recipient cells. In this review, we describe these secretion systems and how their complexities and functions reflect differences in the destinations, states, functions, and sizes of the translocated cargos as well as the architecture of the bacterial cell envelope. We examine the secretion systems from the perspective of pathogenic bacteria that proliferate within plant tissues and highlight examples of translocated proteins that contribute to the infection and disease of plant hosts.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.266
Teacher spread0.243 · 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