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Record W3118413250 · doi:10.1080/14614103.2020.1867038

Elite Food Between the Late Middle Ages and Renaissance: Some Case Studies from Latium

2021· article· en· W3118413250 on OpenAlexaff
Francesca Alhaique, Claudia Moricca, Lia Barelli, Alessia Masi, Raffaele Pugliese, Laura Sadori, Giuseppe Romagnoli, Lavinia Piermartini, Luca Brancazi, Federica Gabbianelli, Giovanni Chillemi, Alessio Valentini

Bibliographic record

VenueEnvironmental Archaeology · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsMusée de la Civilisation
FundersMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsEliteThe RenaissanceMiddle AgesResidenceBioarchaeologyArchaeologyPeriod (music)Consumption (sociology)ZooarchaeologyGeographyAncient historyGrave goodsFood consumptionHistoryArtSociologySocial scienceDemographyAgricultural economicsPolitical scienceArt history

Abstract

fetched live from OpenAlex

The study of plant and animal remains from archaeological sites provides important evidence about past human diets and habits: this includes species selection, food preparation, consumption and disposal practices. Furthermore, such information may also provide inferences about social status. Data from refuse disposal features identified in some elite contexts in central Italy – a high-status residence in Celleno Vecchio (Viterbo) and the Baglioni-Santacroce castle in Graffignano (Viterbo), both in northern Latium, as well as the Santi Quattro Coronati ecclesiastical complex in Rome – allow to explore, using archaeobotanical, archaeozoological and genetic data, some of the different ways in which people expressed wealth by means of food during a period between the late Middle Ages and Renaissance.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.214
Teacher spread0.186 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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