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Record W2078327730 · doi:10.1063/1.2012477

Susan Caroline Bayliss

2005· article· en· W2078327730 on OpenAlexaboutno aff
Andrei Sapelkin

Bibliographic record

VenuePhysics Today · 2005
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsnot available
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

Susan Caroline Bayliss was due to join Queen Mary College, University of London, as a professor of nanotechnology. Sadly, she was involved in a fatal car accident near Manchester, UK, on 16 October 2004, just two weeks before she would have taken that position.Born in Ludlow, England, on 2 December 1954, Sue studied physics at King’s College London and graduated, with honors, in 1976 with a BSc. She worked for her PhD under Yao Liang at the University of Cambridge and submitted her thesis on symmetry dependence of optical transitions in layered materials. She obtained her doctorate in 1980 and remained in Cambridge as a research fellow at Lucy Cavendish College until 1985.For the next five years Sue was a postdoc at Leicester University, where among other things she used her expertise in optical spectroscopy to study light transmission through adult and neonatal eyelids in vivo for a local hospital. That ability to cross boundaries between disciplines had become the trademark of her research ever since. Sue then accepted a lectureship at Loughborough University; she remained there until 1994. During her tenure at Loughborough, she developed her close association with the Daresbury Laboratory synchrotron radiation source and applied a range of structural methods for materials research.Her research matured and she established her reputation as a capable scientist and original thinker at De Montfort University, where she accepted a job in 1994 as a senior lecturer. She worked in areas at the forefront of modern science: on combined structural and optical methods using synchrotron radiation under high pressure and on porous light-emitting silicon and bioelectronic systems. Her collaboration with biologists resulted in a series of pioneering publications on the interaction of nanostructured silicon with living neurons. In 1997 Sue was appointed a professor at De Montfort—and was one of the few female professors in physics in the UK at the time. She developed numerous links with researchers from Canada, France, Russia, Sweden, and the UK, which led to friendships and exchange trips. Her contributions to many areas of science were recognized: She was a member of several bodies that define the strategy of UK and European science and was an elected member of the European High Pressure Research Group Committee, an organization that promotes high-pressure research in Europe through annual meetings and awards. Sue shared her fascination with the properties of light through a series of lectures she delivered to local schools. Topics ranged from glowworms to luminescent nanostructures, and the series proved to be quite a success.An open-minded person who inspired her colleagues and students, Sue opposed fitting people into categories. Beyond science, she had a passion for sport and an ear for music. She played piano and flute, and sang in a rock band. An accomplished rower since her Cambridge years, she had fun racing rowing machines against men in a local gym, finishing first most of the time.Sue never stopped exploring. She was young in spirit and grasped life with all her heart. Her inner energy and unorthodox approach to life and science, best reflected in the following poem she wrote, provided an enviable example for those who came into contact with her: Susan Caroline Bayliss PPT|High resolution I can’t leave behind what I want toAll I can do is force a forgetAct indifferent and substituteSome of me, for embittermentBut enough. None will knowSo why should I be fretful?I have to do far too much nowTo waste time being reflectful.© 2005 American Institute of Physics.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.966

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)0.0000.001

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 head0.053
GPT teacher head0.328
Teacher spread0.275 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2005
Admission routes1
Has abstractyes

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