Bibliographic record
Abstract
Present day complex systems with dependence between their components require more advanced models to evaluate their reliability. We compute the reliability of a system consisting of two subsystems$S_{1}$, and$S_{2}$connected in series, where the reliability of each subsystem is of general stress-strength type, defined by$\Re_{1}=P({\bf A}^{T}{\bf X}>{\bf B}^{T}{\bf Y})$.${\bf A}$&${\bf B}$are column-constant vectors, and strength${\bf X}$& stress${\bf Y}$are multigamma random vectors, i.e.$({\bf X},{\bf Y})\sim MG({\mmb \alpha},{\mmb \beta})$, where${\mmb \alpha}$and${\mmb \beta}$are k-dimensional constant vectors. A Bayesian approach is adopted for$\Re_{2}=P({\bf B}^{T}{\bf W}\geq 0)$, where${\bf W}$is multinormal, i.e.${\bf W}\sim MN({\mmb \mu},{\bf T})$, with the mean vector${\mmb \mu}$, and the precision matrix${\bf T}$having a joint$s$-normal-Wishart prior distribution. Final computations are carried out by simulation, an approach which plays a major role in this article. The results obtained show that the approach adopted can deal effectively with the dependence between components of${\bf X}$&${\bf Y}$.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".