Sampling Strategy of Bubble Characteristics in a 1:2 Scale Curved Continuous Casting Mold: Parametric and Prediction Study
Bibliographic record
Abstract
Argon gas injection in slab continuous casting is common practice to counter SEN clogging phenomena. Bubble characteristics determine the probability of bubble-driven defects such as steel cleanliness, liquid steel reoxidation, and sliver and blister defects. 1:2 scaled water model studies were performed with the help of an advanced high-speed-high-resolution camera shadowgraph imaging technique. Bubble Sauter mean diameter and count were calculated using Trainable Weka segmentation, a machine learning image-based segmentation in the ImageJ platform for different processing conditions such as gas flow rate, liquid flow rate, mold width, and submerged entry nozzle (SEN) depth. A predictive model was developed on the experimental data using an artificial neural network (ANN) algorithm to optimize the bubble mean diameter and count sampling strategy. The model performance is optimized based on the cross-validated adjusted R2. The model shows significant promise with bootstrapping aggregation, five-fold cross-validation, and improved accuracy.
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 | 0.000 |
| 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 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".