Bibliographic record
Abstract
This article proposes to perform data fusion by using an adaptive weighted likelihood function when data sets are available from related populations. The main objective of data fusion is to integrate information from different sources to improve the quality of inference when the sample size from the target population is small or moderate. The weighted likelihood function is employed simply as an instrument to facilitate the data fusion process. The weighted likelihood method has informationtheoretic justification and embraces the widely used classical likelihood method which utilizes only on the data set from the target population. The degree of information integration in the proposed data fusion process is determined by the likelihood weights which should be chosen in a reasonable and adaptive way. The major challenge in the proposed data fusion process is then to choose likelihood weights adaptively and effectively when the deterministic relationships among all related parameters are unknown. We propose adaptive likelihood weights based on the estimated likelihood ratio. We show that the data fusion involving all relevant data sets could significantly improve the mean squared error (MSE) of the classical maximum likelihood estimator which only uses data set from the target population. It also increases the power for hypothesis testing. The proposed estimator is shown to be consistent and asymptotically normally distributed in the framework of generalized linear models. The advantage of the proposed weighted likelihood estimator for linear models is illustrated numerically by a simulation study. A real data example is also provided.
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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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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, 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".