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
As the software projects become more complex, the release deci-sion is made without resolving all the bugs in the issue tracking system. Accumulation of the bugs in the bug repository is similar to nancial obligation as we borrow time and resources to engage in another activity rather than resolving the bugs. Deferring the bug in the next release may have some consequences. Therefore, the decision whether to resolve the bug in the current release or postponing it to the next release is a crucial decision. In this proposal, we study the deferred bugs (lingering bugs) against the non-deferred bugs (regular bugs). Our aim is to develop the pre-dictive model which can predict whether the bug would linger or not. Additionally, we are interested in measuring of the linger-ing bug in terms of principal (standard time it takes to x them) and risk of liability (impact). We propose to use reinforcement learning for prioritization of lingering bugs with respect to their impact.
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 | 0.292 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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