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Record W2900761061 · doi:10.15278/isms.2018.tc08

ENANTIOMERIC EXCESS MEASUREMENTS USING MICROWAVE THREE-WAVE MIXING

2018· article· en· W2900761061 on OpenAlexaboutno aff
Martin Holdren, Luca Evangelisti, Brianna Mitchell, Golara Haghtalab, Patrick Buoniconti, James M. Dittman, Kevin Mayer, Arthur Wu, Caitlin Embly, Brooks H. Pate

Bibliographic record

VenueProceedings of the 73rd International Symposium on Molecular Spectroscopy · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectron Spin Resonance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMixing (physics)MicrowaveFour-wave mixingMaterials sciencePhysicsOpticsNonlinear opticsQuantum mechanics

Abstract

fetched live from OpenAlex

Fluorine- and chlorine-containing molecules in the atmosphere are very strong greenhouse gases, meaning that even small amounts of these gases contribute significantly to the radiative forcing of climate. In addition, a number of these molecules, such as chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs), are harmful to the Earth's ozone layer and for this reason their use is regulated by the 1987 Montreal Protocol. The recent Kigali Amendment has added hydrofluorocarbons (HFCs) to the list of controlled substances, coming into effect on 1 January 2019. HFCs, which do not deplete stratospheric ozone, were introduced as refrigerant replacements for CFCs and HCFCs. They are potent greenhouse gases, with global-warming potentials many times greater than carbon dioxide, and are increasing in the atmosphere at a very fast rate. \n\n\n\nA number of satellite instruments, in particular the ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer), can monitor many of these species by detecting infrared radiation that has passed through the Earth's atmosphere. However, the quantification of their atmospheric abundances crucially requires accurate quantitative infrared spectroscopy. This talk will focus on new and improved laboratory spectroscopic measurements for a number of important halogenated species.

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 score0.013
Threshold uncertainty score0.875

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 science0.0010.000
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 head0.022
GPT teacher head0.284
Teacher spread0.262 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

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