Bibliographic record
Abstract
Preliminaries A channel is an abstract model describing how the received (or retrieved) data is associated with the transmitted (or stored) data. Channel coding starts with Claude Shannon's mathematical theory of communication. Error detection/correction coding Channel coding can be either error detection coding or error correction coding. When only error detection coding is employed, the receiver can request a transmission repeat, and this technique is known as automatic repeat request (ARQ) . This requires two-way communications. An ARQ system requires a code with good error-detecting capability so that the probability of an undetected error is very small. Forward error correction (FEC) coding allows errors to be corrected based on the received information, and it is more important for achieving highly reliable communications at rates approaching channel capacity. For example, by turbo coding, an uncoded BER of 10 −3 corresponds to a coded BER of 10 −6 after turbo decoding. For applications that use simplex (one-way) channels, FEC coding must be supported since the receiver must detect and correct errors, and no reverse channel is available for retransmission requests. Another method using error detection coding is error concealment. This method processes data in such a way that the effect of errors is minimized. Error concealment is especially useful for applications that carry data for subjective appreciation, such as speech, music, image, and video. Loss of a part of the data is acceptable, since there is still some inherent redundancy in the data.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".