Teaching Data Acquisition. An Undergraduate Experiment in the Advanced Analytical Chemistry Laboratory
Bibliographic record
Abstract
Computers may be the most influential technological development in human society in the last quarter century. Computers are used to control instruments, to acquire and to process data, and to communicate results in meaningful ways. Understanding how to use computers empowers students, freeing them from the "black box" approach to instrument operation. The ability to program, to understand the basic steps involved in the analog-to-digital conversion process, and to grasp how the acquisition process itself affects the outcome of an experiment are important elements in the education of future chemists. For the last few years, we have taught data-acquisition within the framework of three laboratory experiments combined with lectures in the Instrumental Analysis II course. Last year, we changed the software part of the exercise by replacing TurboPascal with Matlab. This powerful programming language integrates analog data acquisition via a toolkit with mathematical-oriented computing and easy visualization. The change has greatly simplified the experiment and allowed us to include new data-acquisition concepts and signal processing that would not have been possible before in a reasonable time frame. The article will present the new version of the experiment together with suggestions for its practical implementation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.026 |
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".