Replication and Characterization of the Causally Ambiguous Duration-Sorting (CADS) Effect
Bibliographic record
Abstract
It is generally assumed that information about the exact nature of truly random events can only be obtained after those events occur. One empirical apparent contradiction of this assumption is the causally ambiguous duration-sorting (CADS) effect, in which photon absorptions are measured before a truly random decision about the duration of an experiment is made. The only parameter varied across experimental runs is the duration between on- and off-times, yet the number of photons absorbed prior to this decision is related to the decision itself. This report focuses on further examining the CADS effect by characterizing the pre-decision periods for data continuously recorded for 365 days in an independent laboratory. A complex but reliable periodicity gave a conservative estimate of 4.7 for sigma across six comparisons of pre-decision photon absorptions with post-decision duration as the parameter. A linear CADS equation emerged to estimate magnitude at peak frequencies for each of four equiprobable post-decision durations. An apparently novel and unrelated relationship between photon absorptions and lunar phase was also revealed in the year-long dataset. Determining whether accurate pre-decision information about future durations is only available in retrospect requires further experimentation, but these results strongly support apparent retrocausality or at least causal ambiguity in groups of photons with shared classical boundaries in time.
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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 | 0.027 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".