Bibliographic record
Abstract
Abstract It is often useful to transform a measured signal into a form that is more easily interpreted. Thus, in voltammetry, where the measurement is of a current responding to a perturbation of the electrode potential, the complications arising from diffusional transport may be readily alleviated through the use of semioperators or convolutions. The use of semiintegration enables the voltammetric current to be transformed into an alternative signal that, in the case of a planar electrode, is linearly related to the concentrations of reactant and product at the electrode surface. Numerous advantages of semiintegration have been widely demonstrated. However, semiintegration is but a special case of the more general procedure of convolution. Convolving the voltammetric current with specifically designed functions of time allows the benefits of semiintegration to be applied to other electrode geometries and other experimental circumstances. The prime motive for semiintegration or convolution is to gain access to information about concentrations at the electrode surface. This information opens the door to the measurement of such quantities as bulk concentrations, rate constants, standard potentials, and diffusivities, as well as suggesting mechanisms and enabling the calibration of electrodes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".