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Record W3010436374 · doi:10.1002/jms.4511

A simple method for rapid calibration of faraday and ion‐counting detectors on movable multicollector mass spectrometers

2020· article· en· W3010436374 on OpenAlexaff
Donald W. Davis

Bibliographic record

VenueJournal of Mass Spectrometry · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDetectorCalibrationChemistryFaraday cupDead timeFaraday cageMass spectrometryOpticsAnalytical Chemistry (journal)IonElectron multiplierPhotomultiplierComputational physicsPhysicsChromatographyMagnetic fieldIon beam

Abstract

fetched live from OpenAlex

Methods are presented for rapid determination of relative efficiencies of Faraday cups in a multicollector array with movable cups and of Daly or electron multiplier detector dead time and gain values. The Faraday calibration approach is based on measuring the same isotopic ratio in two sequences with different collector configurations having one collector in common. Changes in thermal fractionation during the two measurement sequences are monitored using the same two collectors. Since the relative efficiencies are determined by measuring the same ratio in different cups corrected for time-dependent changes in fractionation, it is unnecessary to use a standard of known composition and calculations are simple, not requiring the solution of multiple equations. Determination of dead time and gain values for a Daly detector are shown based on multidynamic measurements of masses 207, 206, and 208 from Pb standard SRM982 in two sequences consisting of L1-Daly-H1 and Daly-H1-H2. This provides two equations that can be solved for Daly dead time and gain. This method uses static measurements and is therefore insensitive to signal instability. It also does not require use of a standard of known isotopic composition. The potential of using known cup efficiencies to help determine absolute isotopic abundances is discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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.

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

Citations9
Published2020
Admission routes1
Has abstractyes

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