Bibliographic record
Abstract
Introduction As pointed out in the previous chapter, binary digits (or bits) “0” and “1” are used simply to represent the information content. They are abstract (intangible) quantities and need to be converted into electrical waveforms for effective transmission or storage. How to perform such a conversion is generally governed by many factors, of which the most important one is the available transmission bandwidth of the communication channel or the storage media. In baseband data transmission, the bits are mapped into two voltage levels for direct transmission without any frequency translation. Such a baseband data transmission is applicable to cable systems (both metallic and fiber optics) since the transmission bandwidth of most cable systems is in the baseband. Various baseband signaling techniques, also known as line codes, have been developed to satisfy a number of criteria. Typical criteria are: (i) Signal interference and noise immunity Depending on the signal sets, certain signaling schemes exhibit superior performance in the presence of noise as reflected by the probability of bit error. (ii) Signal spectrum Typically one would like the transmitted signal to occupy as small a frequency band as possible. For baseband signaling, this implies a lack of high-frequency components. However, it is sometimes also important to have no DC component. Having a signaling scheme which does not have a DC component implies that AC coupling via a transformer may be used in the transmission channel. (iii) […]
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.062 |
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".