The phantom SPA method: an inventory problem revisited
Bibliographic record
Abstract
It is widely accepted today that the infinitesimal perturbation analysis (IPA) method for estimating sensitivities is the preferred method, when it is applicable. The major problem with IPA is handling certain kinds of discontinuities, such as thresholds. The smoothed perturbation analysis (SPA) method was conceived applying a conditional expectation to a dynamic system, similar to the filtered Monte Carlo simulation. Conditioning smoothes out the discontinuities and then IPA can be applied to the conditional estimator. Since this alternative estimator has been partly integrated through the conditioning, some knowledge about the underlying distribution is required. When this is not available, SPA estimators require additional estimation. Traditionally, this has been implemented via offline simulations that produce independent replications of a difference process. We propose to bypass this operation by using parallel phantom systems: replicas of the original system that are conditional to the critical events of interest yet use common random numbers instead of independent replications. We show how the efficiency can dramatically improve from the gain in correlation (variance reduction) as well as the gain in computational effort (random variables are generated once and used for all parallel phantoms).
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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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".