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Record W2617454629 · doi:10.3390/dj5020019

Dental Wear: Attrition, Erosion, and Abrasion—A Palaeo-Odontological Approach

2017· review· en· W2617454629 on OpenAlexaff
G.H. Sperber

Bibliographic record

VenueDentistry Journal · 2017
Typereview
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTooth wearAttritionAbrasion (mechanical)DentistryMedicineGeologyOrthodonticsArchaeologyMaterials scienceGeographyComposite material

Abstract

fetched live from OpenAlex

This paper reviews the surface ablation of early hominin teeth by attrition, abrasion, and erosive dental wear. The occurrence of these lesions is explored in a sample of South African fossil australopithecine dentitions revealing excessive wear. Interpretation of the nature of the dietary components causing such wear in the absence of carious erosion provides insight into the ecology of the Plio-pleistocene epoch (1-2 million years ago). Fossil teeth inform much of the living past by their retained evidence after death. Tooth wear is the ultimate forensic dental evidence of lives lived.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.201
GPT teacher head0.413
Teacher spread0.212 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations39
Published2017
Admission routes1
Has abstractyes

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