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Record W2341948057 · doi:10.1080/00268976.2016.1171918

A CRDS sputter-source experiment to study MH radicals: application to NiH and NiD

2016· article· en· W2341948057 on OpenAlexaff
Georgi Dobrev, Jérôme Morville, D. W. Tokaryk, Amanda Ross, P. Crozet

Bibliographic record

VenueMolecular Physics · 2016
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of New Brunswick
FundersNational Institutes of Health
KeywordsZeeman effectChemistryCavity ring-down spectroscopySpectral lineMoleculeSpectroscopyAbsorption (acoustics)HydrideAtomic physicsMetalAbsorption spectroscopyAnalytical Chemistry (journal)Materials scienceMagnetic fieldOpticsPhysics

Abstract

fetched live from OpenAlex

Signatures of metal hydride molecules appear in the optical spectra of cool stars. The observed spectra are used not only for identification of the molecule, but also to assess the abundance of the metal from which the molecule is composed, and to measure the strength of the magnetic field in which the molecule is immersed through the Zeeman splitting of individual spectral lines. Metal hydrides are short-lived radicals, often produced via an electrical discharge, and their steady-state concentrations in a sample are low. High-sensitivity probing techniques, like laser-induced fluorescence, are often appropriate, but (typically much less sensitive) absorption techniques are more useful to assess metal abundances. We describe here a cavity ring-down spectroscopy experiment, usually used to detect absorptions from stable molecules, to collect spectra with very high sensitivity and reproducibility from prototypical metal hydrides NiH and NiD. We have constructed an optical cavity of high finesse (F = 60,000) into which a sputtering source is inserted, and have employed optical fibre and a rigid mounting scheme to keep the ring-down mirrors in alignment during an experiment and between days. We compare our NiH/NiD absorption data with literature results, and highlight some of the strengths and weaknesses of this approach.

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 score0.361
Threshold uncertainty score0.515

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 head0.006
GPT teacher head0.274
Teacher spread0.268 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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