Bibliographic record
Abstract
Harmonic Synchrony Subjects.zip Dataset to recreate portions of Figures 7 and 8. These datasets are MATLAB workspaces which contain simultaneous measurements of the earth-ionosphere ultra-low frequency (ULF) activity and 19 channel quantitative electroencephalographs for 3 subjects. Subjects marked local indicate that ULF activity was monitored simultaneously in Sudbury, Canada while non-local indicates that the e-field measured in Cumiana, Italy. See links below for live data from both stations. The variables in the workspaces are: 1) Raw_19ChanEEG_Mag_Refiltered Contains the raw data (19 brain leads + magnetometer) which has been filtered with eegfiltfft.m between 1.5 and 40 Hz. First 19 columns indicate brain channels 1. Fp1 2. Fp2 3. F7 4. F3 5. Fz 6. F4 7. F8 8. T3 9. C3 10. Cz 11. C4 12. T4 13. T5 14. P3 15. Pz 16. P4 17. T6 18. O1 19. O2 20. Induction Coil Magnetometer 2) srate Sampling rate used during data collection 3) Fig(x) left/right This indicates the temporal periods (in seconds) in the time series from which left or right components of Figures 7 or 8 can be reproduced exactly. 4) cRMS The integrated root-mean-square of the posterior channels which can be derived using the MATLAB expression >>[new variable]=transpose(sqrt(mean(transpose(x(:,13:19).^2))))"] where x is the "Raw_19Chan..." variable. 5)Mag This is the induction coil magnetometer/Marconi antenna data only 6) cRMS_Mag This is the integrated cRMS and induction coil data organized into two rows (1=cRMS, 2=mag). It is this signal that can be entered into the cross-coherence function within EEGLab.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.072 |
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".