Bibliographic record
Abstract
The authors have presented a method that can be potentially used for records of any duration, with any number of channels, any number of channel groupings (different topologies), and in a variety of situation (ICU, sleep, coma, etc.). Unlike methods such as compressed spectral arrays, the proposed method presents samples of original EEG that represent the long-term EEG along with their temporal distribution. Because the actual EEG is presented to the user, no new interpretive skills are required and the method can be employed by anyone familiar with EEG. Moreover, the simple graphical display allows a non-EEG specialist to identify abnormal changes-the emergence of focal changes, bursts or sustained asymmetries, gradual or sudden changes, and cycling of EEG patterns. Quick identification of problems will allow such personnel to contact the EEG specialist for a detailed assessment. The compact nature of the resulting display allows the compressed results to be transmitted via modem or fax to the EEGer at a remote site for an initial assessment of the urgency of the situation. It is important to note that the proposed method is intended to provide a summary of the EEG and should be used to supplement the EEG. It is not intended to replace usual EEG interpretation. The authors' have thus far examined the feasibility of this method in an offline application. Clearly, it will be most advantageous when used online. The authors' future work involves the adaptation of this method for online application.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".