Bibliographic record
Abstract
In data analysis, selecting a proper statistical model is a challenging issue. Upon the selection, there are other important factors impacting the results. In this article, two statistical models, a General Linear Model (GLM) and the Ratio Estimator will be compared. Where applicable, some issues such as heteroscedasticity, outliers, etc. and the role they play in data analysis will be studied. For reducing the severity of heteroscedasticity, Weighted Least Square (WLS), Generalized Least Square (GLS), and Feasible Generalized Least Square (FGLS) will be deployed. Also, a revised version of FGLS is introduced. Since these issues are data dependent, shrimp effort data collected in the Gulf of Mexico for the years 2005 through 2018 will be used and it is shown that the revised FGLS reduces the impact of heteroscedasticity significantly compared to that of FGLS. The data sets will also be checked for the outliers and corrections are made (where applicable). It is concluded that these issues play a significant role in data analysis and must be taken seriously. Further, the two statistical models, that is, the GLM and the Ratio Estimator are compared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".