Power Reconstruction of Fuel Rods by Support Vector Regression for CANDU Reactors
Bibliographic record
Abstract
A support vector regression (SVR) model has been presented for reconstructing fuel rod powers from Canada deuterium uranium core calculations performed with a coarse-mesh finite difference diffusion approximation and single-assembly lattice calculations. The SVR is to nonlinearly map the original data into a higher dimensional feature space. Parameters related to the SVR are optimized by a genetic algorithm using the partial core calculation results of two 6 times 6 fuel bundle models (for training data). Verification has been conducted for two other partial core benchmark problems composed of 6 times 6 and 3 times 3 fuel bundles (for test data). The reconstructed fuel rod powers are compared with the reference solutions obtained with the detailed collision probability calculations using the HELIOS lattice analysis code. It is known from simulation results that the proposed rod power reconstruction algorithm is accurate, yielding the error due to the reconstruction scheme of less than 0.35%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".