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Record W2262781098 · doi:10.1002/pmic.201600047

Key players in neurodegenerative disorders in focus—New insights into the proteomic profile of Alzheimer's disease, schizophrenia, ALS, and multiple sclerosis—24th HUPO BPP Workshop

2016· article· en· W2262781098 on OpenAlexaboutno aff
Andreas Schrötter, Young Mok Park, Katrin Marcus, Daniel Martins‐de‐Souza, Peter Nilsson, Fouzi El Magraoui, Helmut E. Meyer, Lea T. Grinberg

Bibliographic record

VenuePROTEOMICS · 2016
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)ProteomeDiseaseNeuroscienceMedicineBioinformaticsPsychiatryPsychologyBiologyPathology

Abstract

fetched live from OpenAlex

The HUPO Brain Proteome Project (HUPO BPP) held its 24th workshop in Vancouver, Canada, September 29, 2015. The focus of the autumn workshop was on new insights into the proteomic profile of Alzheimer's disease, schizophrenia, ALS and multiple sclerosis.

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)0.006
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.002

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 head0.031
GPT teacher head0.270
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2016
Admission routes1
Has abstractyes

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