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Record W3185528344 · doi:10.3390/ani11082214

What Happened in That Pit? An Archaeozoological and GIS Approach to Study an Accumulation of Animal Carcasses at the Roman Villa of Vilauba (Catalonia)

2021· article· en· W3185528344 on OpenAlexaboutno aff
Lídia Colominas, Pere Castanyer, Joan Frigola Torrent, Joaquim Tremoleda i Trilla

Bibliographic record

VenueAnimals · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
Fundersnot available
KeywordsAssemblage (archaeology)PrehistoryTaphonomyContext (archaeology)ArchaeologyGeographyQuarter (Canadian coin)Period (music)Experimental archaeologyArt

Abstract

fetched live from OpenAlex

Some of the deposits of animal remains documented throughout prehistory and history are clearly something other than ordinary waste from meat consumption. For the Roman period and based on their characteristics, these assemblages have been classified as butchery deposits, raw material deposits, deposits created for the hygienic management and disposal of animal carcasses, or ritual deposits. However, some are difficult to classify, and the parameters that define each of them are not clear. Here, we present a unique deposit from the Roman villa of Vilauba (Catalonia). A total of 783 cattle remains were found in an irregular-shaped 187 m2 pit originally dug to extract the clay used in the construction of the villa walls around the third quarter of the 1st century AD. The application of a contextual taphonomy approach, with the integration of archaeozoological variables, stratigraphy and context, and a GIS analysis, allowed us to document the nature and formation of this singular assemblage. It consisted of the carcasses of 14 cattle individuals from which the meat had been removed to take advantage of it by preserving it. Therefore, the parameters that characterise the refuse of this activity are presented here as a baseline for other studies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.887

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.104
GPT teacher head0.309
Teacher spread0.205 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

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