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Record W2501168753 · doi:10.1039/9781849731997

Biomedical Applications of Synchrotron Infrared Microspectroscopy

2010· book· en· W2501168753 on OpenAlexaboutno aff
Gwyn Williams, Sheila E. Fisher, Josep Sulé-Suso, John M. Chalmers, Gianfelice Cinque, Hugh J. Byrne, Ganesh D. Sockalingum, Nicholas Stone, Fiona M. Lyng, Ehsan Gazi, Peter Gardner, Peter Lasch, Wolfgang Petrich, Larry Carr, Lisa M. Miller, Paul Dumas, Mark J. Tobin, Ariane Kretlow, Janina Kneipp, Dieter Naumann, Bayden R. Wood, Don McNaughton, Hoi‐Ying N. Holman, Francis G. Blankenberg, Henry H. Mantsch

Bibliographic record

Venuenot available
Typebook
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsnot available
Fundersnot available
KeywordsSynchrotronTerminologyBeamlinePresentation (obstetrics)Field (mathematics)Context (archaeology)Synchrotron radiationComputer scienceData scienceEngineering ethicsMedical physicsPhysicsMedicineOpticsEngineeringBiologyMathematicsRadiology

Abstract

fetched live from OpenAlex

Publication of a multi-author textbook on the biomedical applications of synchrotron infrared microspectroscopy was a central element in the workplan of the EU project DASIM (Diagnostic Applications of Synchrotron Infrared Microspectroscopy). The project involved nearly 70 scientists and clinicians from 9 European countries, including all synchrotron facilities that have or are planning an infrared beamline. Together with its international associates from the USA, Canada and Australia, the project brought together essentially all recognized experts in the field. The project aims were to coordinate international research effort and to disseminate the relevant information amongst biological researchers and health care professionals and this multi-author textbook was conceived as the most important measure towards the aim of dissemination. The field of biomedical applications of synchrotron IR microspectroscopy, which has recently seen unprecedented growth, is extremely interdisciplinary, involving synchrotron physicists, spectroscopists, biologists and clinicians, with associated difficulties in getting these experts to understand each other. This multi-author book, from leading world experts, presents all aspects of the field in language that all the disparate experts involved can understand. It demystifies the subject both for clinicians and biologists who find synchrotron physics difficult to understand and for physicists who find medical/biological terminology incomprehensible. The book focuses specifically on biomedical IR spectroscopy using synchrotron light sources with particular emphasis on understandable presentation of necessary background knowledge, digestible summaries of research progress and above all as a practical 'how to do it' guide for those working in or wishing to enter the field of biomedical synchrotron IR microspectroscopy and imaging. Key features of the book include:- * a 'Fundamentals' section, explaining the basics of synchrotrons and FTIR spectroscopy as well as the needs of clinicians and biologists with respect to these technologies * a 'Technical Aspects' section, going into depth on optical issues, sample preparation and study design/data analysis * case studies bringing together these 2 elements through practical examples * Raman microspectroscopy, as an alternative approach, is explored in depth * the foreword is written by Henry Mantsch and Gwynn Williams, the two undisputed experts in the fields of biomedical FTIR spectroscopy and synchrotron IR microspectroscopy respectively

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: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.010

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 head0.004
GPT teacher head0.295
Teacher spread0.290 · 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

Citations52
Published2010
Admission routes1
Has abstractyes

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