Bibliographic record
Abstract
This study was partially supported by grants MH 06686 from the National Institute of Mental Health, NCC 2-1244 from the National Aeronautics and Space Administration, and EIA-0130352 from the National Science Foundation to Robert Kozma. Programming was by Brian C Burke. Essential contributions to surgical preparation and training of animals, data acquisition, and data analysis by John Barrie, Gyöngyi Gaál, and Linda Rogers are gratefully acknowledged. The aim of this study was to measure and classify spatial patterns in sensory cortical EEGs relating to conditioned stimuli (CSs) in order to test the hypothesis, based on clinical reports, that cortical dynamics is not continuous but operates in steps that resemble frames in a cinema. Recent advances in the application of the Hilbert Transform to intracranial recordings of the EEG in animals have revealed markers for repetitive phase transitions in neocortex at frame rates in the theta band. The frames were sought in multichannel EEGs that had been recorded from 8x8 high-density arrays that were fixed on primary sensory cortices of rabbits trained to discriminate visual, auditory or somatic conditioned stimuli with reinforcement (CS+) or without (CS-). Localization of frames in EEGs was by use of a new index, He(t), called "pragmatic information". Each spatial pattern was measured by the 64 scalar analytic amplitudes
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 | 0.003 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.342 | 0.152 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".