MK4: PROGRAMA PARA SÍNTESE DE FUNÇÕES MAJORITÁRIAS COM ATÉ QUATRO VARIÁVEIS DE ENTRADA.
Bibliographic record
Abstract
Com a evolu¸c˜ao da tecnologia e miniaturiza¸c˜ao os CIs (Circuitos Integrados) com tecnologia CMOS (Complementary Metal-Oxide Semicondutor) tˆem se tornado cada vez menores e mais eficientes. Para minimizar ainda mais os circuitos digitais, novas tecnologias s˜ao apresentadas, como por exemplo a tecnologia QCA, que em conjunto com a l´ogica majorit´aria consegue diminuir o tamanho de um circuito. Neste trabalho implementou-se o programa denominado MK-4 que tem como proposta realizar a s´intese de fun¸c˜oes majorit´arias com at´e quatro vari´aveis, utilizando o mapa de Karnaugh. A fim de avaliar o programa desenvolvido em rela¸c˜ao ao custo da fun¸c˜ao minimizada, os resultados obtidos foram comparados em termos de n´umero de n´iveis, n´umero de portas majorit´arias, n´umero de entradas e n´umero de inversores, com os resultados obtidos pelo programa Exact. Foram geradas todas as 65.536 fun¸c˜oes de 4 vari´aveis e o programa MK-4 foi capaz de gerar 43, 57% fun¸c˜oes de menor custo, 13, 97% fun¸c˜oes de custo equivalente e 42, 46% fun¸c˜oes de maior custo quando comparadas com as fun¸c˜oes geradas pelo Exact.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".