Bibliographic record
Abstract
Up to this point, we have considered mathematical properties and problems of inference for progressively censored samples when a particular censoring scheme is to be employed. In reading this far, perhaps you yourself have asked the question, “How does a practitioner decide what the censoring scheme should be?” Is the decision made strictly on the basis of convenience, or can we choose a scheme which makes the most sense in some more statistical or mathematical setting? The question of choosing optimal values of R1, R2, ⋯, Rm when considering a progressive Type-II right censoring scheme is certainly an important one to consider from a practical point of view, and as it turns out, it also gives rise to a number of interesting mathematical problems, in the areas of optimization, numerical analysis, simulation and programming, among others. We consider progressively Type-II right censored samples for the most part, since Type-II censored samples are far more tractable and interesting to consider from the point of linear inference and other mathematical properties, as we have already seen, and right censored samples will arise most frequently in life-testing applications, where it is possible and generally sensible to observe and monitor failures from the onset of experimentation at time t = 0.
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.
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 | 0.019 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".