Caracterização de competitividade de pregões eletrônicos por meio de mineração de dados
Bibliographic record
Abstract
Para adquirir serviços ou produtos os órgãos do Governo Federal brasileiro devem realizar uma licitação. Dentre as modalidades de compra contempladas pela licitação está o pregão eletrônico que, assim como as demais, está sujeito a fraudes. Os comportamentos dos fraudadores modificam-se constantemente e detectá-los é uma tarefa complexa. A presente pesquisa tem como objetivo aferir o grau de competitividade dos pregões eletrônicos por meio da utilização de padrões obtidos pela aplicação de técnicas de Mineração de Dados (MD) no Data Warehouse (DW) das compras governamentais brasileiras. O principal resultado alcançado foi a caracterização de grupos de pregões com diferentes graus de competitividade, a partir de indicadores especificados para este fim, com base no conhecimento de especialistas do ramo.
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How this classification was reachedexpand
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".