MétaCan
Menu
Back to cohort
Record W3208632558 · doi:10.1038/s41597-021-01067-7

Geolocation of unpublished archaeological sites in the Peruvian Amazon

2021· article· en· W3208632558 on OpenAlexafffund
Oliver T. Coomes, Santiago Rivas Panduro, Christian Abizaid, Yoshito Takasaki

Bibliographic record

VenueScientific Data · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicAmazonian Archaeology and Ethnohistory
Canadian institutionsUniversity of TorontoMcGill University
FundersUniversity of TorontoJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologySocial Sciences and Humanities Research Council of CanadaGovernment of CanadaMcGill University
KeywordsGeolocationAmazon rainforestGeographyArchaeologyWorld Wide WebBiologyEcologyComputer science

Abstract

fetched live from OpenAlex

Published maps identifying archaeological sites in the Amazon basin show a paucity of sites in western Amazonia compared to the Brazilian Amazon. Whereas fewer than two dozen are identified for the Peruvian Amazon on basin-wide maps, a thorough review of unpublished archival material held by the Ministry of Culture of Peru and other sources revealed more than 400 known but unpublished sites in the Department of Loreto, challenging the notion that the region was sparsely occupied in prehistory. Our database provides the geolocation of each site and corresponding references for use by scientists seeking to better understand regional Pre-Columbian human occupation and settlement, cultural change, resource use and their landscape legacies. These data are foundational not only to the development of a richer understanding of prehistory and historical ecology of the Amazon basin but importantly for informing current land use, forest conservation and development policies as well as initiatives to support indigenous land and cultural rights in Amazonia.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.144
GPT teacher head0.288
Teacher spread0.145 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueScientific DataSame topicAmazonian Archaeology and EthnohistoryFrench-language works237,207