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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".