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Record W1609752520 · doi:10.1255/ejms.1377

Ancient Pottery from Archaeological Sites in Southern Italy: First Evidence of Red Grape Product Markers

2015· article· en· W1609752520 on OpenAlexaff
Giuliana Bianco, Sara Granafei, Fabio Colivicchi, Tommaso R. I. Cataldi, Alessandro Buchicchio

Bibliographic record

VenueEuropean Journal of Mass Spectrometry · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsQueen's University
FundersUniversità degli Studi della BasilicataRegione Basilicata
KeywordsPotteryArchaeologyArchaeological scienceGeographyAncient historyHistory

Abstract

fetched live from OpenAlex

The chemical analysis of tartaric acid (TA) and syringic acid (SA), as grape product markers in ancient ceramic vessels from the sites of Manduria and Torre di Satriano (southern Italy), was successfully performed. Firstly, the fragmentation behaviour of TA and SA as deprotonated molecules, [M-H](-), obtained by collision-induced dissociation, was investigated. Then, reversed-phase liquid chromatography (RPLC) with electrospray ionization (ESI) in negative ion mode, using a quadrupole linear ion trap in multiple reaction monitoring (MRM), was employed. A binary mobile phase composed of water-acetonitrile with 0.1% (v/v) acetic acid enabled the optimum ESI efficiency of SA, greatly improving its identification when it occurs in trace amounts. Chemical analysis of ancient pottery fragments is a valid method for establishing the existence of preserved organic residues, which is valuable new evidence for the culture and customs of ancient populations, in this case those of southern Italy. The proposed RPLC-ESI-MRM method allowed a systematic investigation of ceramic fragments of both archaeological sites, thus providing positive evidence for the presence of TA and SA as grape product markers in storage vessels dating back to the ninth to third centuries BC.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0020.000

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 head0.075
GPT teacher head0.235
Teacher spread0.160 · 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.

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
Published2015
Admission routes1
Has abstractyes

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