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Record W4410191533 · doi:10.2218/jls.7309

Bolas, projectile points and hunting technologies in Southern Patagonia (Santa Cruz, Argentina) during the Late Holocene

2024· article· en· W4410191533 on OpenAlexfundno aff
Agustín Agnolín

Bibliographic record

VenueJournal of Lithic Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsnot available
FundersAgencia Nacional de Promoción Científica y TecnológicaSecretaría de Ciencia y Técnica, Universidad de Buenos AiresGoldcorpUniversidad de Buenos Aires
KeywordsProjectile pointHoloceneArchaeologyGeologyGeographyPhysical geography

Abstract

fetched live from OpenAlex

The late Holocene in the central-western region of the Santa Cruz province (Southern Patagonia, Argentina) was characterized by the occurrence of climatic and environmental change processes which implied a progressive drought, markedly recorded after ca. 3000 years BP. Peopling processes in the region during this period implied mobility strategies that included residential mobility in lowlands combined with logistical mobility in highlands. In this changing context, local hunter-gatherer groups used a variety of weapons for obtaining prey: bolas, bow and arrow and spears or darts. In this context, this work analyses the distribution and chronology of the lithic components of these technologies (arrow and spear/dart stone points and stone balls) including evidence of their manufacture (preforms and raw materials). For this, we consider different environments (different low basins and high basins and plateaus) and different types of sites (open air site, basaltic wall, rock shelter, hunting blind and burial). The aim is to assess the role of these technologies in the context of changes in mobility strategies and landscape use. The evidence collected suggests that these three technologies had important differences in their use and production, especially in their replacement potential, manufacturing possibilities, transport and repair. These differences possibly made them complementary. Our main argument suggests that hunter-gatherers diversified technologies, specifically those used to obtain prey, in a context of landscape and resource fragmentation. Therefore, there was a complementary but differential use of technologies and not a process of adoption and abandonment. New technical options were incorporated into previously established ones, thus offering greater flexibility to the subsistence system.

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)0.001
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 score0.179
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.324
Teacher spread0.289 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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