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Record W3110767234 · doi:10.1038/s41598-020-78490-0

The mineralogy and structure of use-wear polish on chert

2020· article· en· W3110767234 on OpenAlexfundno aff
Patrick Schmidt, Alice Rodriguez, Kaushik Yanamandra, Rakesh K. Behera, Раду Йовита

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMineralogy and Gemology Studies
Canadian institutionsnot available
FundersDeutsche ForschungsgemeinschaftYork University
KeywordsGeologyMineralogyGeochemistry

Abstract

fetched live from OpenAlex

Polished edges of archaeological stone tools are commonly investigated to obtain information on the tools' uses in prehistory. Yet to this day, it remains unclear what exactly such polishes are and how they form. Answering these questions should allow the elaboration of new interpretative methods based on objective measurements. Two major competing hypotheses of polish formation have been proposed: abrasion and the formation of a thin amorphous film on the chert or flint surface. We employ reflectance infrared spectroscopy, a technique particularly sensitive to thin amorphous films, to investigate these two hypotheses. We found no added amorphous layer that would have formed upon friction against bone, antler, ivory or wood. Our observations suggest polish formation by abrasion, notwithstanding previous claims of added amorphous surface structures. This has implications for our understanding of the physical processes taking place during friction of chert and flint against different materials. Our results also open the possibility to propose new pathways for identifying different use-wear processes, based on the degree of abrasion.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.207
Teacher spread0.187 · 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 designBench or experimental
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

Citations23
Published2020
Admission routes1
Has abstractyes

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