Prototype testing and algorithm development for the Cosmic Ray Inspection and Passive Tomography (CRIPT) project
Bibliographic record
Abstract
The Cosmic Ray Inspection and Passive Tomography (CRIPT) collaboration has completed the testing of small muon detector prototypes and has commenced construction of a 12 layer, 4m2prototype muon scattering tomography system. Three areas of CRIPT's progress are reported: (1) results from the testing of one of drift chamber muon detector prototypes; (2) algorithms for muon momentum estimation and tomographic image reconstruction; and (3) the status of the large prototype construction. The intrinsic resolution of the 2.4 m long, 1.2 m wide drift chamber muon detector prototype has been measured to be 1.73 mm perpendicular to the anode wire, and 2.9 mm parallel to the anode. A Bayesian estimator algorithm has been developed for muon momentum estimation. From simulations, the momentum resolution is expected to be highly asymmetric, varying from −18% to +92% integrated across the cosmic ray muon spectrum. A novel Point-of-Closest-Approach (PoCA) algorithm has also been developed for tomographic imaging. Multiple possible muon trajectories are assumed for each muon. The expected completion date for the construction is summer 2012, with first tomographic data following soon afterward.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".