Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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]
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.
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 | 0.001 | 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 | 0.001 | 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 it