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Record W4402740305 · doi:10.1016/j.jasrep.2024.104755

Investigating connectivity in the Metapontine chora using Least Cost Path

2024· article· en· W4402740305 on OpenAlexaff
Christine Davidson

Bibliographic record

VenueJournal of Archaeological Science Reports · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiffusion and Search Dynamics
Canadian institutionsMcMaster UniversityTrent University
Fundersnot available
KeywordsPath (computing)GeographyGeologyEconomic geographyArchaeologyComputer scienceComputer network

Abstract

fetched live from OpenAlex

• Landscape archaeology helps to characterize ancient Greek territories. • Several linear anomalies in the landscape of Metaponto were likely ancient roads. • Digitized path-finding (Least Cost Path) allows us to visualize ancient travel. • Sanctuaries in the countryside acted as spaces for assembly and administration. • Communities formed in the countryside, using sanctuaries as places of interaction. Ancient sites identified through the surface-level collection of artifacts in the countryside of the Greek settlement of Metaponto reveal a collection of extra-urban necropoleis, sanctuaries, and farmsteads. Topographical anomalies identified in aerial photography of this area also suggest a possible system of land division. Using Least Cost Path, routes most likely used for travel are identified, many of which overlap the anomalous “division lines.” These routes represent potential roads between farmsteads and rural sanctuaries of Metaponto in the 5th-3rd centuries BCE, and their interaction with the “division lines” reveals their potential use within a system of property delineation in the chora (‘countryside’). The formation of rural communities centered upon these rural sanctuaries is also explored.

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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.340
Teacher spread0.302 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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