Bibliographic record
Abstract
Abstract A method of correcting for instrumental drift must be associated with any calibration procedure in order to validate the stored calibration data (slopes and intercepts) over a long period of time. Indeed, it can usually be observed that the drift is negligible for a 24 h period, but over longer periods corrections to the measured intensities have to be made. These corrections are based on the measurement of special specimens known as drift monitors or simply monitors. Physically, the monitor can comprise one to several specimens, each containing one or several analytes of which the intensity of each analyte is slightly higher than the highest intensity in the analyte concentration range. The other two essential properties of a monitor are its stability over time and reproducibility of its intensity measurements. A drift correction must be applied to the measured intensities of every analyte using one, two or several monitors depending on the spread of the intensity ranges. Some drift correction methods are proposed and it is explained how to combine them with the calibration procedure in order to obtain precise analytical results over long periods of time from the same set of calibration data. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".