análise sobre os fatores que influenciaram no resultado financeiro dos investimentos realizados pelo FMIEE CRIATEC
Bibliographic record
Abstract
O objetivo deste artigo é analisar os investimentos realizados pelo Fundo Criatec, buscando identificar quais fatores influenciaram o seu resultado financeiro. Para isso, foram determinadas métricas de retorno financeiro dos investimentos e variáveis para a análise dos fatores qualitativos e quantitativos que influenciaram o resultado dos investimentos do Criatec. Cada um dos fatores foi relacionado com o resultado financeiro, através da análise de cluster hierárquico sendo determinados grupos de empresas com caraterísticas similares e com mais chance de retornos positivos. Os resultados indicaram que ter sócios com qualificação ou experiência em gestão se mostrou um fator expressivo no desempenho das empresas apoiadas pelo Criatec. Outro fator diferencial foi o grau de inovação,sendo que as empresas com tecnologias mais disruptivas apresentaram um resultado financeiro inferior às empresas com produtos sem grandes diferenciais tecnológicos.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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".