Bibliographic record
Abstract
The problem of determining whether or not the value of an attribute is caused by other observed attributes, or they merely happen to occur together, has been attacked from different angles. In this paper we propose a solution to the problem of distinguishing between causal and acausal temporal rules, and the system that generated the rules. The proposed method, called the Temporal Investigation Method for Enregistered Record Sequences (TIMERS) is explained and introduced formally. TIMERS assumes that time can flow in two directions, forward, which is the natural flow, and backward. This method has been implemented in the TimeSleuth software. We assume that the input to TIMERS consists of a sequence of records, where each record is observed at regular intervals. A set of rules is then generated from this input data. We perform three tests. One to determine if the set of rules describes an instantaneous relationship, where the decision attribute depends on condition attributes seen at the same time instant. The other two tests determine the degree to which a set of rules is causal or acausal by changing the direction time when generating temporal rules. The results of the three tests are then used to declare a verdict as to the nature of the system: Instantaneous, causal, or acausal. Unlike approaches based on causal Bayesian networks, our approach does not emphasize relations among individual attributes. Its verdict applies to a set of rules, and the system that has generated the temporal input data. 1.
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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 | 0.050 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".