Bibliographic record
Abstract
Generalized perturbation theory (GPT) can be used as a means to evaluate sensitivity coefficients or to approximate variations in integrated lattice parameters resulting from small changes in local cell properties. Using a first-order perturbation approach, the changes in the integral parameters can be written as a sum of a direct term that takes directly into account the variations in the cell properties and an indirect term that approximates the neutron flux variations resulting from the perturbation. For a lattice cell code that relies on a collision probability technique to solve the transport equation, a problem related to the evaluation of the perturbed transport operator also arises because the collision probability matrix depends on the total cross section. A technique is presented to simulate these variations in the collision probability matrix using approximate source term variations. Comparison with exact calculations will show that the results obtained using GPT with these approximate source terms are reliable provided the perturbations remain small. Results for a parametric study of a two-dimensional pressurized water reactor 17 × 17 assembly and void reactivity calculations for a DUPIC-fueled CANDU cell are also presented.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".