Conceptual Model for Automatic Proofreading of Technical Documents
Bibliographic record
Abstract
This study deals with a set of issues related to the development of a conceptual model for automatic proofreading of technical documentation.The purpose of this study is to investigate the prospects for creating the software for automatic proofreading of text documents with an assessment of the prospects for its subsequent implementation in various areas of scientific cognition and in activities of various educational institutions.The methodological approach is a combination of a systematic study of modern algorithms for checking technical documents with an analysis of the prospects for building a concept for creating an optimal model for automatic document proofreading.The main results of this study should be the definition of the main areas for the development of issues for the creation of the concept under consideration and identification of the constituent elements of the conceptual model for automatic proofreading of technical documentation, which is important from the standpoint of ensuring the proper level of quality of functioning of such a system.The prospects for further research in this area are determined by the relevance of the stated topic conditioned by the urgent need to develop and implement an effective system for verifying technical documents as soon as possible.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".