A simple method for rapid calibration of faraday and ion‐counting detectors on movable multicollector mass spectrometers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".