Prototype testing and algorithm development for the Cosmic Ray Inspection and Passive Tomography (CRIPT) project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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, 4m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> prototype 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it