FIRST IMAGES FROM THE CRIPT MUON TOMOGRAPHY SYSTEM
Bibliographic record
Abstract
The CRIPT Cosmic Ray Imaging and Passive Tomography system began data taking in September 2012. CRIPT is a “proof of principle” muon tomography system originally proposed to inspect cargo in shipping containers and to determine the presence of special nuclear materials. CRIPT uses 4 layers of 2 m x 2 m scintillation counter trackers, each layer measuring two coordinates. Two layers are used to track the incoming muon and two for the outgoing muon allowing the trajectories of the muon to be determined. The target volume is divided into voxels, and a Point of Closest Approach algorithm is used to determine the number of scattering events in each voxel, producing a 3D image. The system has been tested with various targets of depleted uranium, lead bricks, and tungsten rods. Data on the positional resolution has been taken and the intrinsic resolution is unfolded with the help of a simulation using GEANT4. The next steps include incorporation of data from the spectrometer section, which will assist in determining the muon's momentum and improve the determination of the density of the target.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".