Bibliographic record
Abstract
Fulfilled people are rare in this world. Rather, people spend their lives dealing with stress, anxiety, sickness, and so on. From my pool of more than 350 subjects, I found people who had an SI near one were rare. In short, those who have the willingness to do something, or are highly motivated, and are active during their EKGs, generally have an SI close to one (1/f rhythm). The rate was less than half (Table 5-1, Figure 5-5). So many people exhibited a lower SI. I have recorded the heartbeats of people from Iran, Indonesia, Japan, America, Canada, Australia, Germany, Russia, and so forth. And SI more or less indicates a person's state of health. Specifically, unhappy states were detectable through a combination of interviews and mDFA. When interviewing people, I observe and make notes, then interpret the mDFA results later. But this is a hard task. I hope someone invents an mDFA device that would make it easier to manage daily life. The SI is a numerically expressed quantitative measure, the scaling exponent, which is the outcome of mDFA computation, which was first innovated by people such as Peng, Stanley, Goldberger, Ivanov, Glass, and others.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.249 | 0.062 |
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".