Combining CT scan and particle imaging techniques: applications in geosciences.
Bibliographic record
Abstract
A small scale physical model of a river and its bed \nwas built to study sediment transport. This model was installed \nthrough a CT scanner in order to validate a data acquisition \nsystem coupling a CT scan and a particle image velocimetry \n(PIV) system. The PIV structure is fixed to the scanner, which \nmoves along 2.6 meters rails. This combined system provides \nhigh spatial and temporal resolution measurements of bed \ndensity and fluid velocity. The data acquisition is time-synchronized \nand co-located greatly improving our \nunderstanding of the dynamics inside the scanned object. The \nbed topography and porosity as well as the fluid velocity profiles \nnear the bed were successfully derived. These parameters are \nessential to link hydrodynamic processes over the bed and \nsediment transport. The methodology holds promising \nadvancements in experimental sedimentology, and could also find \ninteresting applications in other non-medical fields.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".