Faster, Simpler, and More Precise Calibration Curves: Expanding the Scope of Continuous Calibration
Bibliographic record
Abstract
Accurate calibration curves are essential for quantitative analysis but are often neglected due to their time, cost, and labour demands. Traditional calibration involves measuring solutions of known concentrations to construct a calibration curve relating analyte concentration to instrument response. While these relationships rarely follow simple closed-form equations, linear approximations are commonly used for simplicity, often being fitted with too few data points for high-accuracy calibration. Continuous calibration was previously developed to address these challenges by continuously infusing a concentrated calibrant solution into a clean matrix solution while monitoring the response in real time. This approach significantly reduces time and labour while generating extensive data, improving calibration precision and accuracy. Despite its advantages, method limitations and technical complexities have hindered widespread adoption. Here, continuous calibration is expanded and simplified with modern accessible equipment, open-source code, and a user-friendly web tool which streamline data processing, generating smoothed and equation-fitted calibration curves complete with quality-of-fit and dynamic range estimates. This method was applied to a broad range of systems and analytical techniques, including external, standard addition, and internal standardization calibrations, and mass spectrometry, and infrared and ultraviolet–visible spectroscopies, with the latter yielding molar absorption coefficients from a single experiment. By reducing time and effort while enhancing precision, these advancements have the potential to improve experimental quality and efficiency across numerous fields.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".