Bibliographic record
Abstract
Production events and uncertainties impact ladle processing times, which in turn affect the steel temperature at the tundish. Deviations from the desired steel temperature in the tundish can result in production stoppage either due to premature solidification or due to liquid steel leakage at the caster. These stoppages are extremely costly and must be avoided by any means. Using a good method for estimation of heat losses that is linked to production parameters is very useful to reduce the risk of incorrect steel temperature. Most of the literature to date on computing the ladle heat loss is concerned with analytical and numerical solutions that are interesting academically or with regards to ladle design. This paper however is concerned with building a gross heat loss estimation model for a ladle in daily operation. It studies the important parameters that affect final steel temperature and uses actual production data to validate the analyses and conclusions. A rule of thumb for the cooling rate of steel in a ladle is developed with an average value of 0.55 to 1.40°C/min. [Received 25 December 2015; Accepted 22 February 2017]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".