Distinguishing causal and acausal temporal relations
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. In this paper we propose a solution to the problem of distinguishing between causal and acausal temporal sets of rules. The method, called the Temporal Investigation Method for Enregistered Record Sequences (TIMERS), is explained and introduced formally. The input to TIMERS consists of a sequence of records, where each record is observed at regular intervals. Sets of rules are generated from the input data using different window sizes and directions of time. The set of rules may describe an instantaneous relationship, where the decision attribute depends on condition attributes seen at the same time instant. We investigate the temporal characteristics of the system by changing the direction of time when generating temporal rules to see whether a set of rules is causal or acausal. The results are used to declare a verdict as to the nature of the system: instantaneous, causal, or acausal. 1.
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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