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Infrared Absorption Spectroscopy ( <scp>IR</scp> , <scp>FTIR</scp> , <scp>DRIFT</scp> , <scp>ATR</scp> )

2018· preprint· en· W2972761019 on OpenAlexaff
Michael B. Toffolo, Francesco Berna

Bibliographic record

VenueThe Encyclopedia of Archaeological Sciences · 2018
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicBuilding materials and conservation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsInfrared spectroscopyFourier transform infrared spectroscopyInfraredSpectroscopyAbsorption (acoustics)Characterization (materials science)ExcavationArchaeologySedimentary depositional environmentChemistryGeologyAnalytical Chemistry (journal)MineralogyMaterials scienceNanotechnologyChemical engineeringEnvironmental chemistryPaleontologyOrganic chemistryPhysicsEngineeringHistoryOpticsComposite material

Abstract

fetched live from OpenAlex

Infrared absorption spectroscopy is a versatile analytical method that can identify different compounds in archaeological materials and sediments by measuring the portions of the infrared spectrum absorbed by specific molecules. This technique was developed in the 1930s for the characterization of industrial materials, and then applied to the study of artifacts and works of art starting from the 1950s. In the last three decades, infrared spectroscopy has become an established tool in archaeological fieldwork, where it is used to obtain real‐time information regarding the archaeological contexts under excavation. Infrared spectrometry can determine the presence of crystalline and disordered inorganic materials, as well as organic materials in archaeological sediments, and thus it is a valuable method in addressing problems related to the preservation of the archaeological record, site formation processes, post‐depositional alterations, stratigraphic correlations, absolute chronology, pyrotechnology, and past human activities.

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)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0020.007
Scholarly communication0.0010.001
Open science0.0050.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.246
Teacher spread0.222 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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