Transfer Zero-Entropy and Its Application for Capturing Cause and Effect Relationship Between Variables
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Detection of causality is an important and challenging problem in root cause and hazard propagation analysis. It has been shown that the transfer entropy approach is a very useful tool in quantifying directional causal influence for both linear and nonlinear relationships. A key assumption for this method is that the sampled data should follow a well-defined probability distribution; yet this assumption may not hold for some industrial process data. In this paper, a new information theory-based measure, transfer 0-entropy (T0E), is proposed for causality analysis on the basis of the definitions of 0-entropy and 0-information without assuming a probability space. For the cases of more than two variables, a direct T0E (DT0E) concept is presented to detect whether there is a direct information and/or material flow pathway from one variable to another. Estimation methods for the T0E and the DT0E are addressed. The effectiveness of the proposed method is illustrated by two data sets, one based on data from a pilot scale process and a second evaluation based on data from a benchmark industrial case study.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it