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Record W3048558674 · doi:10.15278/isms.2020.tg05

AUTOMATIC ASSIGNMENT AND INTERNAL ROTATION WITH PGOPHER

2020· article· en· W3048558674 on OpenAlexaboutno aff
Colin M. Western, Brant Billinghurst

Bibliographic record

VenueProceedings of the 2020 International Symposium on Molecular Spectroscopy · 2020
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceRotation (mathematics)Artificial intelligence

Abstract

fetched live from OpenAlex

This talk describes recent updates to the \\textsc{pgopher} program\\footnote{C. M. Western, J Quant. Spec. Radiat Trans., 186 221 (2017)} (http://pgopher.chm.bris.ac.uk), including new tools for computer assisted assignment of spectra and simulate spectra involving internal rotation. The new tools for assignment are described in a recent paper\\footnote{C. M. Western and B. E. Billinghurst, Phys. Chem. Chem. Phys., 21 13986 (2019)} and include (I) a method of trying multiple assignments automatically based on the \\textsc{autofit} algorithm of the group of Brooks Pate\\footnote{N. A. Seifert, I. A. Finneran, C. Perez, D. P. Zaleski, J. L. Neill, A. L. Steber, R. D. Suenram, A. Lesarri, S. T. Shipman, B. H. Pate, J Mol. Spectrosc. 312, 13, (2015)}, and (II) a new form of presenting assignments, a nearest lines plot. These latter plots allow possible sets of assignments to be accepted (or rejected) quickly, and also allow the rapid extension of initial assignments to an entire branch or band. Both these tools have been applied to the analysis of high resolution IR spectra, allowing the rapid assignment of ~10,000 lines for a band, even in the presence of strong overlapping transitions. These tools are now being supplemented with tools for handling internal rotation in \\textsc{pgopher}, including a general way of handling the special permutation inversion symmetry that is typically required for such molecules, and calculating levels affected by internal rotation, either by adding empirical terms to a standard asymmetric top Hamiltonian, or a more elaborate approach based on including multiple torsional states. Progress on the development of these tools will be presented, with applications to spectra taken on the far IR beamline of the Canadian light source\\footnote{The Canadian Light Source, is supported by the Canada Foundation for Innovation, Natural Sciences and Engineering Research Council of Canada, the University of Saskatchewan, the Government of Saskatchewan, Western Economic Diversification Canada, the National Research Council Canada, and the Canadian Institutes of Health Research.}.

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 score0.098
Threshold uncertainty score0.442

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)0.0000.000

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 head0.201
Teacher spread0.196 · 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
Published2020
Admission routes1
Has abstractyes

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