New adventures with Schrödinger's cat: The role of statistical uncertainty in stabilizing measurement
Bibliographic record
Abstract
We reopen Erwin Schrodinger's thought experiment involving a cat in an informationally impenetrable box. A common view is that the cat enters a superposition of alive/dead because of a lack of observation, leading to uncertainty about the state of the cat. We, on the other hand, argue that the cat only enters a superposition if everything about the cat is known prior to the box being closed. The superposition results from a lack of uncertainty inside the box. Rather than interpreting this state of affairs as a live and dead cat interacting with each other, we suggest that the more natural interpretation is that of an inability to precisely position events within spacetime due to the lack of uncertainty. We clarify how stable measurement depends on a diversified portfolio of statistical uncertainty, and how the lack of such uncertainty in Schrodinger's box precludes stabilization.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".