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Record W4293824663 · doi:10.1242/jcs.260456

Meeting report – Cell dynamics: host–pathogen interface

2022· article· en· W4293824663 on OpenAlexfundno aff
Charlotte Odendall, Joana Sá‐Pessoa, Francisco S. Mesquita

Bibliographic record

VenueJournal of Cell Science · 2022
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research CouncilDirectorate for Biological SciencesQueen's UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungÉcole Polytechnique Fédérale de LausanneUniversity of OxfordQueen's University BelfastCompany of BiologistsNational Science FoundationRoyal SocietyUniversity College LondonWellcome Trust
KeywordsBiologyPathogenHost (biology)Interface (matter)ConversationImmunologyEcologySociologyCommunication

Abstract

fetched live from OpenAlex

Two years into the most significant infectious disease event of our generation, infections have populated every conversation and in-depth understanding of host-pathogen interactions has, perhaps, never been more important. In a successful return to in-person conferences, the host-pathogen interface was the focus of the third Cell Dynamics meeting, which took place at the glorious Wotton House in Surrey, UK. The meeting organised by Michaela Gack, Maximiliano Gutierrez, Dominique Soldati-Favre and Michael Way gathered an international group of scientists who shared their recent discoveries and views on numerous aspects, including cell-autonomous defence mechanisms, pathogen interactions with host cytoskeletal or membrane dynamics, and cellular immune regulation. More than 30 years into the beginning of cellular microbiology as a field, the meeting exhibited the unique aspect of the host-pathogen interface in uncovering the fundamentals of both pathogens and their 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.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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