Increasing the location rate of positron emission particle tracking (PEPT) measurements for froth flotation
Bibliographic record
Abstract
A non-invasive technique named positron emission particle tracking (PEPT) is used to study the dynamics of mineral particles found in froth flotation. High frequency tracking is required to fully resolve turbulent flow paths with highly fluctuating velocity components. Tracking frequency is also called “location rate”, which is the number of tracked locations per second. A time interpolation algorithm was implemented on the raw data of a PEPT measurement to increase the effective location rate of PEPT data. The algorithm was validated with Monte-Carlo simulations of the Siemens ECAT “EXACT3D” HR++ PET scanner at PEPT Cape Town (University of Cape Town). The paths generated from the simulated data with improved timing information had an order of magnitude lower uncertainty in both the position and time components of a location measurement. The algorithm was then applied to PEPT experiments with freely moving particles in a laboratory scale flotation vessel to demonstrate the impact of tracking with higher fidelity. • Positron emission particle tracking (PEPT) in flotation needs higher location rates. • A time interpolation algorithm was implemented to increase the listmode precision. • The location rate of Lagrangian measurements with PEPT was increased up to 10.0 kHz. • The uncertainty of a location decreased by an order of magnitude in time and position. • The interpolation algorithm was validated with Monte Carlo simulations.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".