Instrumentation-related uncertainty of reflectance and transmittance measurements with a two-channel spectrophotometer
Bibliographic record
Abstract
Spectrophotometers are operated in numerous fields of science and industry for a variety of applications. In order to provide confidence for the measured data, analyzing the associated uncertainty is valuable. However, the uncertainty of the measurement results is often unknown or reduced to sample-related contributions. In this paper, we describe our approach for the systematic determination of the measurement uncertainty of the commercially available two-channel spectrophotometer Agilent Cary 5000 in accordance with the Guide to the expression of uncertainty in measurements. We focus on the instrumentation-related uncertainty contributions rather than the specific application and thus outline a general procedure which can be adapted for other instruments. Moreover, we discover a systematic signal deviation due to the inertia of the measurement amplifier and develop and apply a correction procedure. Thereby we increase the usable dynamic range of the instrument by more than one order of magnitude. We present methods for the quantification of the uncertainty contributions and combine them into an uncertainty budget for the device.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".