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Record W2871134121 · doi:10.5604/01.3001.0012.1800

Mammals in the economy of ancient Porphyreon (Lebanon)

2018· article· en· W2871134121 on OpenAlexaboutno aff
Joanna Piątkowska-Małecka

Bibliographic record

VenuePolish Archaeology in the Mediterranean · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArchaeology and Historical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal husbandryLivestockDonkeyGeographyPopulationArchaeologyPersianPrehistoryAgriculturePeriod (music)Quarter (Canadian coin)Iron AgeVeterinary medicineAncient historyHistoryDemographyArtForestry

Abstract

fetched live from OpenAlex

An archaeozoological analysis of mammal remains recovered from the dwelling units and streets of ancient Porphyreon excavated in 2009, 2010 and 2012, gives insight into the importance of mammals for the residents of this quarter in succeeding periods: from the Iron Age through the Persian and Hellenistic periods to Byzantine times. Husbandry lay at the base of the animal economy and was supplemented with hunting various species of gazelle. Cattle, sheep and goat were the most numerous livestock species represented in the archaeological record. The high percentage of cattle observed in Iron Age deposits could have resulted from the agricultural lifestyle of the population. Starting from the Persian period, sheep and goat played the most prominent role in the animal economy, implying a pastoral model of husbandry. Raising goats for meat was more significant initially; from the Hellenistic period onwards, the number of sheep reared for milk and wool increased. Pigs constituted a minor percentage of the livestock. The presence of equid remains, including horse and donkey, was confirmed for the Persian period, when these animals were used for transportation.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.007
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.259
Teacher spread0.211 · 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 designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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