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Record W2593722081 · doi:10.3233/bsi-150118

The evolution of biomedical vibrational spectroscopy: A personal perspective

2015· article· en· W2593722081 on OpenAlexaff
Henry H. Mantsch

Bibliographic record

VenueBiomedical Spectroscopy and Imaging · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPerspective (graphical)SpectroscopyMaterials scienceNanotechnologyChemical physicsComputer scienceChemistryPhysicsArtificial intelligenceAstronomy

Abstract

fetched live from OpenAlex

When my colleague Parvez Haris asked me to write a historical review on the evolution of biomedical vibrational spectroscopy I consented, but now I need to clarify what this review is about and what it is not.It is my personal belief that biomedical vibrational spectroscopy has yet to reach its full potential and therefore I will not restrict myself to recounting its past history but will look ahead to its future and where it may still be evolving.Accordingly, my review will comprise two parts.As yours truly has now joined the league of octogenarians, in the first part I plan to share with the readers my personal recollections of the very early days of vibrational bio-spectroscopy which I was privileged to witness and be part of.In the second part I intend to peer into the crystal ball and speculate on new applications in the medical sciences, envisioning vibrational spectroscopy as a tool for the exploration of the human mind to probe psychosomatic diseases and emotional disorders. The arduous road to biomedical vibrational spectroscopy: Where did we come from?As is often the case with historical reviews it can be difficult to precisely pinpoint a beginning since this depends on how far back the reviewer is prepared to go.The exploitation of vibrational spectroscopy for use in medicine clearly relies on earlier applications in biology, which go back to previous uses in chemistry, which in turn rest on the solid foundations of vibrational spectroscopy in physics.Having said this I now wish to refer the reader to a few reviews which deal with this progression of vibrational spectroscopy from physics to chemistry to biology to medicine.Four pre-eminent publications, written in 1985 [14], 2009 [1], 2012 [11] and 2014 [4] supply over 700 bibliographic references (with some duplications) that comprehensively document this evolution, allowing this reviewer to focus on events he has personally witnessed and experienced over the past 50+ years.It is fair to state that until the 1950s vibrational spectroscopy was the purview of physics, when a growing number of chemists began to employ it regularly for the elucidation of molecular structures.

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)0.014
metaresearch head score (Gemma)0.017
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.011
Scholarly communication0.0080.011
Open science0.0010.003
Research integrity0.0060.014
Insufficient payload (model declined to judge)0.0040.002

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 head0.009
GPT teacher head0.315
Teacher spread0.306 · 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
GenreCommentary

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

Citations6
Published2015
Admission routes1
Has abstractyes

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