Bibliographic record
Abstract
Abstract Despite digital techniques for data acquisition and processing being widely used in biomedical research for quite some time, inappropriate signal conditioning and digitization are still potential pitfalls threatening both the reliability of the experiments and the proper interpretation of the acquired data. The aim of this chapter is to review: (1) the Sampling (Nyquist) Theorem; (2) the concept of aliasing; (3) the importance of antialiasing low‐pass filtering for eliminating the effect of aliasing and appropriately determining the sampling frequency; (4) the advantages of properly chosen filter cut‐off frequency and slope for determining the minimal required sampling frequency; and (5) the impact of incorrectly selected sampling frequency on the interpretation of biomedical data. In a case study, a model of electrogastrographic (EGG) recording is mixed with a model of electrocardiographic (EKG) artifact in an overall white noise environment. The resulting composite signal is low‐pass filtered and then digitized with a sampling frequency of 1 Hz. The cut‐off frequency of the first‐order low‐pass filter is altered from 0.5 Hz to 0.1 Hz. Amplitude frequency spectra of the digitized recordings are investigated to illustrate the effect of aliasing. An example with a real human electrogastrogram, in which an EKG artifact is present, illustrates the simulation results. When a first‐order antialiasing filter is used, at least a five‐fold difference between the filter cut‐off frequency and the sampling frequency is recommended for compliance with the Sampling Theorem. Increasing the order of the antialiasing filter can reduce the required sampling frequency, but can also make the entire instrumentation system underdamped, thus injecting oscillatory artifacts every time abrupt or sudden changes in the external conditions during the recording occur.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".