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Record W4232108591 · doi:10.1002/9781118941065.ch39

FTIR Microscopy

2017· other· en· W4232108591 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicBuilding materials and conservation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFourier transform infrared spectroscopyFourier transformInfraredMaterials scienceSpectroscopyAnalytical Chemistry (journal)Fourier transform spectroscopyInfrared spectroscopyChemistryOpticsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Fourier transform infrared spectroscopy (FTIR) is used to identify the functional groups (e.g., amide, phosphate, carbonate and hydroxyl) present in organic and inorganic compounds by measuring their absorption of infrared radiation over a range of wavelengths (e.g., 50 to 5000 cm-1 wavenumber). A modern FTIR spectrometer collects and digitizes the interferogram, performs the Fourier transform and displays the FTIR spectrum. The coupling of a FTIR spectrophotometer to an optical microscope produces a system capable of doing FTIR microspectroscopy. The success in application of IR microspectrophotometers and FTIR microscopes to many areas of research (semiconductors, polymers, and pharmaceuticals), as well as forensic investigation, has established this technique as a powerful tool in the analysis of small samples. The application of FTIR analysis to archaeological investigation ranges widely from dating, to use of space, ancient pyrotechnologies, diagenesis and transformation of the archaeological record; all major aspects of site formation processes.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.071
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0730.002

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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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

Quick stats

Citations25
Published2017
Admission routes1
Has abstractyes

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