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
We have developed an entirely new template-automaton-based knowledge database system for an interactive intelligent language tutoring system (ILTS) for Japanese-English translation whereby model translations as well as a taxonomy of bugs extracted from ill formed translations typical of nonnative learners are collected. Unlike conventional rule-based systems whose complicated solution search procedure and labor-intensive processing have led to so-called knowledge engineer bottlenecks of the expert systems, the new dynamic programming-based heaviest common sequence (HCS) matching algorithm is both efficient and robust in which error diagnosis is implemented by selecting, from among many candidates’ paths in the system template, a path having an HCS of a highest similarity with a student's free-format translation input. This best matched path to the given ill formed sentence is used to provide contingent feedback messages. We have laid down a theoretical framework for the global HCS matching algorithm which is applied to the dual form of acyclic weighted digraphs by topologically sorting the template automaton structured according to augmented transition networks. An extensive evaluation test of the diagnostic engine has ensured the validity, efficiency, and robustness of the algorithm in providing error-contingent feedback to a wide spectrum of learners even with different educational backgrounds.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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