Traceable measurements and calibration: a primer on uncertainty analysis
Bibliographic record
Abstract
Abstract Describing the quality of measurements is necessary to understand the level of confidence in any observation. Accuracy, precision, trueness, repeatability, reproducibility, and uncertainty are all used to describe quality of measurement, but the terms are inconsistently defined and measured and thus easily misunderstood. One purpose of quality parameters is for the comparison of observations, but when dissimilar methods for estimating quality terms are utilized, a comparison is misrepresented. A standardized approach to estimating uncertainty provides a basis for meeting measurement requirements and providing a level of confidence for observations. Here, we show the approach used by the National Ecological Observatory Network to estimate uncertainty of the calibration processes and measurements illustrated with an example of uncertainty assessment on a temperature sensor. Detailing the approach for uncertainty assessment provides the transparency necessary for network science and allows for the approach to be adopted in the scientific community. Reporting uncertainty with all measurements needs to become consistent and commonplace across disciplines.
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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 | 0.072 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".