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RSA 2004: Combined Basic Research Satellite Symposium - Session Two: Toll-Like Receptors and Organ Damage

2005· article· en· W2016600296 on OpenAlexaboutno aff
Pranoti Mandrekar, Stephen B. Pruett, Gavin E. Arteel, Geoffrey M. Thiele, Gyöngyi Szabó

Bibliographic record

VenueAlcoholism Clinical and Experimental Research · 2005
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsReceptorTollTLR4Session (web analytics)Toll-like receptorTLR2ImmunologyMedicineInternal medicineComputer scienceInnate immune system

Abstract

fetched live from OpenAlex

This article summarizes the proceedings of the RSA 2004 Combined Basic Research Satellite Meeting convened at the Westin Bayshore Resort and Marina, Vancouver, BC. The session "Toll-like receptors and organ damage" featured three speakers and was chaired by Drs. Steve Nelson and Craig McClain. The presentations were 1) Toll-like receptor mediated macrophage activation-modulation by acute alcohol administration in mice by Stephen Pruett, 2) Alcoholic liver disease: Crossroads of TLRs and oxidative stress by Gavin Arteel, and 3) The role of TLR2 and TLR4-mediated signals in liver injury by Gyongyi Szabo.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0600.019

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 head0.301
GPT teacher head0.557
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2005
Admission routes1
Has abstractyes

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