Utilização de "softwares" estatísticos na interpretação de hipóteses com dados desbalanceados
Bibliographic record
Abstract
Este trabalho teve como objetivo a estruturação de hipóteses e somas de quadrados a elas associadas, com dados desbalanceados, visando orientar a utilização de alguns pacotes estatísticos por usuários não iniciados em estatística. Para tanto, foram revistos métodos de análise de dados desbalanceados, e adotado o modelo estatístico: y = Xe + e, com e sem interação. No desenvolvimento da metodologia, foram estabelecidas as expressões analíticas correspondentes às hipóteses e somas de quadrados associadas, sobre linhas, colunas e interações, bem como, sobre os métodos computacionais envolvidos nas mesmas. Ainda nesse tópico, foram descritos doze pacotes estatísticos, e suas principais características relativamente a tais hipóteses e somas de quadrados foram apresentadas. Através de exemplo numérico, mostrou-se o desempenho desses pacotes no trato com dados desbalanceados, com algumas caselas vazias, expondo-se suas hipóteses e somas de quadrados associadas; e apresentou-se uma comparação entre os resultados por eles fornecidos.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".