Exploring a multi-source fusion approach for genomics information retrieval
Bibliographic record
Abstract
In this paper, we focus on the biomedicine domain to propose a multi-source fusion approach for improving information retrieval performance. First, we consider a common scenario for a metasearch system that has access to multiple baselines with retrieving and ranking documents/passages by their own models. Second, given selected baselines from multiple sources, we employ two modified fusion rules in the proposed approach, reciprocal and combMNZ, to rerank the candidates as the output for evaluation. Third, our empirical study on both 2007 and 2006 genomics data sets demonstrates the viability of the proposed approach to better performance fusion. Fourth, the experimental results show that the reciprocal method provides notable improvements on the individual baseline, especially on the effective passage MAP, the passage2-level and the diversity MAP, the aspect-level.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".