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Early Horizon camelid management practices in the Nepeña Valley, north-central coast of Peru

2015· article· en· W2004839042 on OpenAlexaff
Paul Szpak, David Chicoine, Jean‐François Millaire, Christine D. White, Rebecca Parry, Fred J. Longstaffe

Bibliographic record

VenueEnvironmental Archaeology · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsWestern University
Fundersnot available
KeywordsGeographyPeriod (music)HorizonArchaeologyAnimal husbandryIsotope analysisEcologyBiologyAgriculture

Abstract

fetched live from OpenAlex

South American camelids (llamas and alpacas) were of great economic, social and ritual significance in the pre-Hispanic Andes. Although these animals are largely limited to high-altitude (>3500 masl) pastures, it has been hypothesised that camelids were also raised at lower altitudes in the arid coastal river valleys. Previous isotopic studies of Early Intermediate Period (c. 200 BC–AD 600) and Middle Horizon (c. AD 600–1100) camelids support this argument. Here, we utilise carbon and nitrogen isotopic analyses of camelid bone collagen from the Early Horizon (c. 800–200 BC) sites of Caylán and Huambacho on the north-central coast of Peru to examine the management of these animals during the first millennium BC. Most of the camelid isotopic compositions are consistent with the acquisition of animals that were part of caravans, moving between the coast and the highlands. A small number of the animals may have been raised on the coast, suggesting that the practice of coastal camelid husbandry was in the experimental phase during the Early Horizon before growing into a more established practice in the Early Intermediate Period. These results echo zooarchaeological studies from the region that have revealed a paucity of camelid remains in refuse deposits prior to 800 BC, followed by an increase in abundance after 450 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)0.000
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 score0.009
Threshold uncertainty score0.593

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 studies0.0000.002
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 head0.020
GPT teacher head0.209
Teacher spread0.189 · 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

Citations47
Published2015
Admission routes1
Has abstractyes

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