Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Traceability has always been considered a useful but costly activity and different methods have been applied to reduce this cost. The current paper constitutes an attempt to improve these methods by introducing an optimal traceability process to be used in the context of RTCA DO-297 “Integrated Modular Avionics (IMA) Development Guidance and Certification Considerations”.</div><div class="htmlview paragraph">The paper starts by comparing the definitions of traceability from DO-297 and the related development guidelines (i.e. ARP4754A, DO-254 and DO-178B). The paper continues by classifying the traceability methods recommended by the guidelines and introducing a performance criterion for optimal traceability based on category theory. This criterion addresses the possibility of information loss present in the current traceability methods. The paper proposes an optimal traceability process (i.e. that guarantees that information is not lost) and exemplifies it. The paper ends by recommending further enhancements.</div></div>
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".