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
erta, Canada iii A new concept for ambiguity resolution is introduced. Past techniques determined each ambiguity separately regardless of the assumed integers of the other ambiguities. These techniques considered only the partial relationship among ambiguity parameters themselves, and treated these ambiguities as fixed only if their correct values were known. In this study, the search ranges are determined recursively and are related to each other. To determine the uncertainty range of an ambiguity parameter, the effect of an assumed integer on other ambiguities is fully taken into account by constraining the ambiguities into integers. These constrained integers may be correct or incorrect. However, the incorrect integers are rejected later. All observations from the initial to the current epoch are taken into account by a least-squares filter. Furthermore, an index of the possible inability to fix ambiguities is used. Therefore, the full search of all possible integer ambiguities is not required and the computation time is dramatically reduced. Analysis of experimental results shows significant improvements in the time of ambiguity search and the number of epochs required to resolve the ambiguities. The reliability of the ambiguity resolution is also improved. iv ACKNOWLEDGMENT First, I wish to acknowledge my supervisor, Prof. G. Lachapelle, for his supervision,
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 | 0.000 | 0.000 |
| Open science | 0.000 | 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