Evidence map›Paper›PMID 30458049›Full record

ArticlePloS one2018

AGMT3-D: A software for 3-D landmarks-based geometric morphometric shape analysis of archaeological artifacts.

Gadi Herzlinger, Leore Grosman

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
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  7. Review
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  9. Archaeological evidence for two separate dispersals of Neanderthals into southern Siberia.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Gadi HerzlingerInstitute of Archaeology, Mount Scopus, The Hebrew University of Jerusalem, Jerusalem, Israel.ORCID 0000-0002-6437-607X
Leore GrosmanInstitute of Archaeology, Mount Scopus, The Hebrew University of Jerusalem, Jerusalem, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present here a newly developed software package named Artifact GeoMorph Toolbox 3-D (AGMT3-D). It is intended to provide archaeologists with a simple and easy-to-use tool for performing 3-D landmarks-based geometric morphometric shape analysis on 3-D digital models of archaeological artifacts. It requires no prior knowledge of programming or proficiency in statistics. AGMT3-D consists of a data-acquisition procedure for automatically positioning 3-D models in space and fitting them with grids of 3-D semi-landmarks. It also provides a number of analytical tools and procedures that allow the processing and statistical analysis of the data, including generalized Procrustes analysis, principal component analysis, a warp tool, automatic calculation of shape variabilities and statistical tests. It provides an output of quantitative, objective and reproducible results in numerical, textual and graphic formats. These can be used to answer archaeologically significant questions relating to morphologies and morphological variabilities in artifact assemblages. Following the presentation of the software and its functions, we apply it to a case study addressing the effects of different types of raw material on the morphologies and morphological variabilities present in an experimentally produced Acheulian handaxe assemblage. The results show that there are statistically significant differences between the mean shapes and shape variabilities of handaxes produced on flint and those produced on basalt. With AGMT3-D, users can analyze artifact assemblages and address questions that are deducible from the morphologies and morphological variabilities of material culture assemblages. These questions can relate to issues of, among others, relative chronology, cultural affinities, tool function and production technology. AGMT3-D is aimed at making 3-D landmarks-based geometric morphometric shape analysis more accessible to archaeologists, in the hope that this method will become a tool commonly used by archaeologists.

Indexed as

ArchaeologyComputer SimulationSoftwareStatistics as Topic

Identifiers

PMID30458049
PMCPMC6245792

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.