State-of-the Art Comparability of Corrected Emission Spectra. 2. Field Laboratory Assessment of Calibration Performance Using Spectral Fluorescence Standards
Bibliographic record
Abstract
In the second part of this two-part series on the state-of-the-art comparability of corrected emission spectra, we have extended this assessment to the broader community of fluorescence spectroscopists by involving 12 field laboratories that were randomly selected on the basis of their fluorescence measuring equipment. These laboratories performed a reference material (RM)-based fluorometer calibration with commercially available spectral fluorescence standards following a standard operating procedure that involved routine measurement conditions and the data evaluation software LINKCORR developed and provided by the Federal Institute for Materials Research and Testing (BAM). This instrument-specific emission correction curve was subsequently used for the determination of the corrected emission spectra of three test dyes, X, QS, and Y, revealing an average accuracy of 6.8% for the corrected emission spectra. This compares well with the relative standard uncertainties of 4.2% for physical standard-based spectral corrections demonstrated in the first part of this study (previous paper in this issue) involving an international group of four expert laboratories. The excellent comparability of the measurements of the field laboratories also demonstrates the effectiveness of RM-based correction procedures.
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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 | 0.061 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".