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Record W4366986691 · doi:10.33993/ephnap.2022.32.267

Villa Rustica from Rapoltu Mare-La Vie (Hunedoara County). Preliminary Zooarheological Data

2023· article· en· W4366986691 on OpenAlexaff
Georgeta El Susi, Andrei Gonciar

Bibliographic record

VenueEphemeris Napocensis · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicAncient and Medieval Archaeology Studies
Canadian institutionsOutotec (Canada)
FundersUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii
KeywordsFaunaLivestockGeographyBeaverHunting seasonPredationArchaeologyBiologyForestryEcologyDemography

Abstract

fetched live from OpenAlex

The article analyzes a fauna sample collected from a villa rustica in Rapoltu Mare, Hunedoara County. The material was gathered during the 2014–2017 seasons consisting of 1,406 fragments, 1,049 of which are fragments from the Roman levels (2nd–3rd centuries AD), and 357 are post–Roman fragments. Cattle dominate the Roman Phase I sample with 33.33%, followed by sheep/goats with 22.22% and pigs with 16.66%. Cattle dominate as fragments in the phases II–III, accounting for 32.02%, followed by small ruminants with 28.43% and pigs with 26.47%. The bones of the dog total 1.31%, while those of the horse, 6.21%. Hunting was a recreational activity, used to obtain furs, hides, antlers, with little impact on food supplying. Hunted prey includes hare, roe deer, red deer, beaver and various small carnivores. Sheep and goats account for 34–37% (NISP/MNI) of livestock at the post-Roman level, followed by pigs (26–27%) and bovines (18–24%). The horse has a threefold quota in comparison with the Roman levels, about 13–15%.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.279
Teacher spread0.204 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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