Imaginary Number Probability in Bayesian-type Inference
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
Doubt, choice and probability.Bayesian probability computation is the most significant approach in complex maths interesting for all logicians to understand. And its computation and reasoning set us new priorities in further attempts to develop a more human-type reasoning, where 'possible' and 'probable' scales are matched and sorted out on subjective basis.We use Bayesian computation models, Finetti's principle of free observation, dynamic probability, complex number equations, and other formal-logical principles in order to base our own modeling and sub-branching.We aim to understand relation of the computation frequency in probability inference and in imaginary probability computation. And how the Bayesian inference principle could be disturbed by the possibilities of artificial 'doubt' of imaginary probability. We try to define the common patterns of complex number behavior in probability modeling, and the modeling of such probability in $i$ numbers, so we could say one day that the probability of having a cancer is 1.99 in for 100, and the hypothetical probability of it is none (0).The same subjective manner same subjective manner of a culprit who prefers an idea, or an image over logic, undertaking it as a guidance for his actions; a magnificent specter of a writer, a diamond of an artist and all those things which lure them all to the same jail of a culprit - the split of decision.
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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 | 0.003 | 0.025 |
| 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