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Record W4313545441 · doi:10.1007/s12192-022-01318-5

Second Virtual International Symposium on Cellular and Organismal Stress Responses, September 8–9, 2022

2023· article· en· W4313545441 on OpenAlexaff
Patricija van Oosten‐Hawle, Sarah J. Backe, Anat Ben‐Zvi, Dimitra Bourboulia, Mara Brancaccio, Jeff Brodsky, Melody S. Clark, Giorgio Colombo, Marc B. Cox, Paolo De Los Rios, Frank Echtenkamp, Adrienne L. Edkins, Brian Freeman, Pierre Goloubinoff, Walid A. Houry, Jill L. Johnson, Paul LaPointe, Wei Li, Valérie Mezger, Len Neckers, Nadinath B. Nillegoda, Veena Prahlad, Adam M. Reitzel, Ruth Scherz‐Shouval, Lea Sistonen, Francis Tsai, Mark R. Woodford, Mehdi Mollapour, Andrew W. Truman

Bibliographic record

VenueCell Stress and Chaperones · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversity of AlbertaUniversity of Toronto
FundersNational Institute of General Medical SciencesAgence Nationale de la RechercheNatural Environment Research CouncilSight Research UK
KeywordsTheme (computing)Library scienceGerontologyMedicineComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The Second International Symposium on Cellular and Organismal Stress Responses took place virtually on September 8-9, 2022. This meeting was supported by the Cell Stress Society International (CSSI) and organized by Patricija Van Oosten-Hawle and Andrew Truman (University of North Carolina at Charlotte, USA) and Mehdi Mollapour (SUNY Upstate Medical University, USA). The goal of this symposium was to continue the theme from the initial meeting in 2020 by providing a platform for established researchers, new investigators, postdoctoral fellows, and students to present and exchange ideas on various topics on cellular stress and chaperones. We will summarize the highlights of the meeting here and recognize those that received recognition from the CSSI.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.630

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 head0.249
Teacher spread0.239 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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