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Lipids, pasta and palazzi: The 8th Yeast Lipid Conference, Torino, Italy, 10â12 May, 2007

2007· article· en· W1965892218 on OpenAlexaboutno aff
Gianni Balliano, GÃ ⁄ nther Daum

Bibliographic record

VenueFEMS Yeast Research · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsnot available
Fundersnot available
KeywordsYeastErgosterolBiologyLibrary scienceBiochemistryComputer science

Abstract

fetched live from OpenAlex

In 1995, a group of yeast lipidologists headed by G. Daum (Graz, Austria) founded the ‘Yeast Lipid Conference’ (YLC) with the purpose of sharing regularly the latest findings of their research on yeast lipids. Since then, every second year a European city has hosted the YLC, attracting an increasing number of scientists from European countries as well as from the USA, Canada and India. In May 2007, the 8th YLC took place in Torino, Italy, and was organized by G. Balliano from the Faculty of Pharmacy of Torino University. Sixty-;eight participants attended this meeting and presented 26 regular lectures and 26 posters, some of which were selected for oral presentations. The scientific programme of the 8th YLC included all the topics traditionally presented at this meeting, namely metabolism, transport and regulation of all main classes of lipids in yeast. In the Opening Lecture held at the Universita degli Studi di Torino, Aula Magna del Rettorato, G. Daum, the chairman of YLC, gave a review of the previous YLCs and the recent development of yeast lipid research. In the scientific part of his lecture, he reported novel findings of yeast lipid depot formation from his laboratory. Several presentations addressed the role of proteins involved in sterol and terpenoid biosynthesis as well as in transport. Yeast mutants defective in presqualene sterol biosynthesis were reported by F. Karst (Colmar, France) as a possible source of terpenoid compounds. An update on nonvesicular transport of ergosterol in yeast was presented by A.K. Menon (New York, NY, USA). …

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.634

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 science0.0010.000
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 head0.044
GPT teacher head0.318
Teacher spread0.274 · 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
Published2007
Admission routes1
Has abstractyes

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