ANÁLISE DE MODELOS MATEMÁTICOS PARA O PROBLEMA PROBABILÍSTICO DE LOCALIZAÇÃO- ALOCAÇÃO DE MÁXIMA COBERTURA
Bibliographic record
Abstract
10.12957/cadest.2010.15740 O Problema Probabilístico de Localização-Alocação de Máxima Cobertura (PPLAMC) é uma variação do problema de p-medianas que consiste em localizar facilidades (centros), maximizando o número de usuários atendidos (cobertos) e garantindo um bom nível de serviço. O nível de serviço está relacionado aos parâmetros de fila, ou seja, tempo de espera e quantidade de pessoas aguardando atendimento. Sabendo que os intervalos entre chegadas e atendimento variam segundo uma distribuição de probabilidade, os modelos de otimização combinatória do PPLAMC levam em consideração conceitos da Teoria de Filas. Sendo assim, este trabalho tem como objetivo avaliar modelos matemáticos para o PPLAMC utilizando instâncias disponíveis na literatura.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".