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
The manual signature-based authentication of a large number of documents is a laborious and time-consuming task. Consequently many off-line signature verification sys- tems were recently developed. In this paper we propose a human-centric system, which exploits the synergy between human and machine capabilities, and show that this com- bined system can perform better (than humans or a ma- chine) for almost all operating costs. The combination stra- tegy is based on techniques in receiver operating charac- teristics (ROC) analysis. We conduct an experiment on a data set that contains 765 test signatures from 51 writers, and record the performance of 23 human classifiers, and that of a hidden Markov model-based (HMM-based) clas- sifier, in ROC space. We propose that a manager (human or machine) specifies acceptable operating costs (Neyman- Pearson criterion), after which our human-centric system makes an optimal decision by utilizing the maximum attain- able combined classifier.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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