Bibliographic record
Abstract
Introduction - a simple complex in artificial intelligence and machine learning, B.H. Juang an introduction to hidden Markov models and Bayesian networks, Z. Chahramani multi-lingual machine printed OCR, P. Natarajan et al using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system, U.-V. Marti and H. Bunke a 2-D HMM method for offline handwritten character recognition, H.-S. Park et al data-driven design for HMM topology for online handwriting recognition, J.J. Lee et al hidden Markov models for modelling and recognizing gesture under variation, A.D. Wilson and A.F. Bobick sentence lipreading using hidden Markov model with integrated grammar, K. Yu et al tracking and surveillance in wide-area spatial environments using the abstract hidden Markov model, H.H. Bui et al shape tracking and production using hidden Markov models, T. Caelli et al an integrated approach to shape and colour-based image retrieval of rotated objects using hidden Markov models, S. Muller et al.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".