Bibliographic record
Abstract
Humanistic Intelligence (HI) is defined an having two embodying elements. (1) It is a signal processing framework in which the human and the computer use each other as peripherals in a feedback loop. (2) The HI processing apparatus is Inextricably intertwined with the oat, natural capabilities of the human mind and body. Biofeedback (or biocybernetic) interfaces made possible through analysis of physiological signals, such as electroencephalograms (EEG), electrocardiograms (ECG) skin conductance (SC), blood pressure (BP) and respiration can provide a means to realize HI. The HI-Comp project builds upon previous work (e.g. the WearComp project) to develop a wearable computer that is physiologically responsive. HI-Comp requires signal analysis research In both offline and online situations to develop appropriate pattern detection algorithms. EEG signal analysis can be performed using Fast Fiourier Transform (FFT) for power analysis, and the Hilbert Transform for phase analysis. The first HI-Comp, the HI-Cam, is a wearable personal imaging application of HI that uses FFT power analysis on the EEG signal to control various features of an biological Interfaces that will lead to true extensions of the mind and body.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".