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Record W2901839367 · doi:10.1002/rcm.8360

1 <sup>st</sup> European Mass Spectrometry Conference (EMSC)

2018· editorial· en· W2901839367 on OpenAlexaboutno aff
Dietrich A. Volmer

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2018
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGermanLibrary scienceMass spectrometryChemistryArt historyArtComputer scienceHistoryArchaeologyChromatography

Abstract

fetched live from OpenAlex

The 1st European Mass Spectrometry Conference (EMSC) was held on 11–15th of March 2018 in Saarbrücken, Germany. It was jointly hosted by the French and German Mass Spectrometry Societies and held in lieu of the French and German national MS conferences, but intentionally expanded to a wider European format. Attended by over 650 participants, the conference featured almost 100 oral presentations and over 300 poster presentations, including 12 plenary talks. All presentations were selected after rigorous review by a scientific committee using a point-based system. The highest scoring abstracts for oral presentation were chosen as keynote lectures for every scientific session. Before the official opening of the conference on Sunday night, several well-attended workshops and short courses took place; namely, short courses on “Computational and bioinformatics tools for proteomics”, “Novel and established approaches to high-performance mass spectrometry imaging”, “Cross-linking and HDX-MS: Towards the study of interacting domains in biological complexes”, “Synthetic Polymers: MS towards the megadalton range & ion mobility MS”, “CE-MS coupling: new developments and applications”, and “Data analysis in metabolomics”. Alain van Dorsselaer from the Université de Strasbourg gave the opening Wolfgang Paul lecture on the future of mass spectrometry. Other invited plenary lecturers included Philippe Schmitt Kopplin (Helmholtz Zentrum München, Germany), Arnaud Delcorte (Université Catholique de Louvain, Belgium), Gert von Helden (Fritz-Haber-Institute, Berlin, Germany), Gérard Hopfgartner, (University of Geneva, Switzerland), Thomas Kraemer (University of Zurich, Switzerland), Liam McDonnell (Fundazione Pisana per la Scienza ONLUS, Italy), Ljiljana Pasa-Tolic (Pacific Northwest Laboratory, Richland, WA, USA), Zoltan Takats (Imperial College, London, UK), Pierre Thibault (Université de Montréal, Quebec, Canada) and Joelle Vinh (Biological Mass Spectrometry and Proteomics, ESPCI Paris, France). In addition to the regular program, the Young Scientists interest group of the French MS society organized a Young Mass Spectrometrists' special event on Monday night, with several presentations and a competition for an oral presentation in the regular EMSC program. David Weigt (Hochschule Mannheim) won this lecture spot. Scientific awards were presented to Tanja Bien (Universität Münster), Ina Brümmer (Universität Stuttgart), Carsten Engelhardt (Universität Siegen), Patrick Helmer (Universität Münster), Johanna Hofmann (FU Berlin), Michal Sharon (Weizmann Institute), Baptiste Schindler (University of Lyon) and Alessandro Vetere (MPI für Kohleforschung). Prizes were also awarded for the best poster presentations during the conference. Rene Zangl (Universität Frankfurt) Alexander Potthoff (Universität Münster) and Waldemar Hoffmann (FU Berlin) were chosen as winners. Finally, all presenters were invited to submit a paper to this special conference issue of Rapid Communications in Mass Spectrometry based on the research shown at the EMSC conference. This special issue contains these submissions, which have gone through the regular RCM peer-review process and thus meet the journal's high scientific and editorial standards.

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)0.004
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: Editorial · Consensus signal: none
Teacher disagreement score0.239
Threshold uncertainty score0.798

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0060.003
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.2390.256

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 head0.020
GPT teacher head0.281
Teacher spread0.262 · 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
GenreEditorial

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
Published2018
Admission routes1
Has abstractyes

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