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Record W7163755167 · doi:10.5281/zenodo.17613768

LA DETECCIÓN ARQUEOLÓGICA EN SELVAS SUBTROPICALES: PROSPECCIONES EXPERIMENTALES INTENSIVAS EN EL PARQUE NACIONAL IGUAZÚ (PROVINCIA DE MISIONES, ARGENTINA)

2025· article· es· W7163755167 on OpenAlexaboutno aff
Luciano Perez Pesce, María Florencia Núñez, Eduardo Apolinaire

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languagees
FieldArts and Humanities
TopicAmazonian Archaeology and Ethnohistory
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationQuarter (Canadian coin)Context (archaeology)

Abstract

fetched live from OpenAlex

Desde el año 2019 se vienen desarrollando investigaciones arqueológicas sistemáticas en el norte de la provinciade Misiones, las cuales incluyen dentro de sus objetivos la detección de contextos arqueológicos ubicados enlas diferentes unidades ambientales que presenta el área. Estas tareas han permitido detectar numerosos sitiosarqueológicos nuevos, la gran mayoría localizados en áreas asociadas al sector de riberas del río Iguazú. Por elcontrario, en los sectores más alejados de los grandes cursos fluviales, las dificultades para detectar materialesarqueológicos son mucho mayores, dada la conjunción de una muy baja visibilidad (producto de la densaflora y el mantillo vegetal que cubre el suelo) y una accesibilidad fuertemente limitada. A fin de establecer siexiste un sesgo en la distribución espacial de los sitios, se realizaron prospecciones intensivas en áreas de bajavisibilidad y difícil acceso que hasta ahora no habían podido ser exploradas. Estas prospecciones permitierondetectar nuevos contextos arqueológicos, ampliar la información disponible sobre la variabilidad arqueológicadel nordeste misionero y comenzar a establecer las bases para comprender los patrones de asentamiento de losgrupos humanos que habitaron este territorio en el pasado prehispánico.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.004

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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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