Results of simulations of a system with the recommendation architecture
Bibliographic record
Abstract
Functionally complex electronic systems are organized into functional components exchanging unambiguous information. The requirement to exchange unambiguous information results in difficulties in implementing parallel processing and extreme difficulty in implementing any capability to heuristically change functionality based on experience. The recommendation architecture allows the exchange of ambiguous information between functional components and therefore offers a way to reduce these difficulties. A system with the recommendation architecture uses a device imprinting mechanism to heuristically organize its inputs into a portfolio of ambiguous information repetition conditions on a range of levels of detail. The presence or absence of these conditions contains enough information to be used by a separate subsystem to determine appropriate behavior. Simulations of a simple system with the recommendation architecture demonstrate that sequences of inputs of wide range of different types can be heuristically organized into a functionally usable set of repetition conditions. Organization is successful even though there are no exact repetitions of input conditions. Learning effectiveness measures which make no use of information on the consequences of system actions can be used to adjust architectural parameters to organize even wider ranges of input types. These results demonstrate the feasibility of developing functionally complex systems with the recommendation architecture.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".