Bibliographic record
Abstract
Abstract Modelling of the screening performance for classification processes is important to obtain a first estimate for a new process in the planning phase. In this work especially the grade efficiency curves of sieve classifications with vibrating screens were examined. A sensitivity study was performed by changing the operating parameters of the sieving machine and the parameters of the charging material (i.e. mass flow, particle size, etc.). The aim was to correlate the input parameters with the grade efficiency curve of the classification process. The main aspect of the presented work is to find an appropriate method to adjust four screening parameters in a way that the measured grade efficiency curve is modelled correctly. Several methods for this adjustment step are reviewed. A sensitivity study using a tumbling screen was performed previously. It is apparent that for that study, different methods and models for the parameter adjustment need to be used. Furthermore it is shown that data reconciliation is necessary, since the mass balance of the particle streams may not be closed correctly. In summary this work is the first step to predict the screening performance of a sieving machine without material‐ and time‐consuming experiments.
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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".