Symmetric sum‐based biometric score fusion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multimodal biometric systems, which combine information from multiple biometric sources, have shown to improve the identity recognition performance by overcoming the weaknesses and some inherent limitations of unimodal systems. A new framework for score level fusion based on symmetric sums (S‐sums) has been presented. These S‐sums are generated via triangular norms. The proposed framework has been tested on two publicly available benchmark databases. In particular, the authors used two partitions of NIST‐BSSR1, i.e. NIST‐multimodal database and NIST‐fingerprint database. The experimental results show that the proposed method outperforms the existing approaches for the NIST‐multimodal database and NIST‐fingerprint database.
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.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.056 | 0.124 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.010 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it