Price predictions of scrap steel for north China via machine learning
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
Historically, projections of the values of different commodities have been relied upon by governments and investors alike. This research examines the difficult task of estimating scrap steel prices, which are released daily for the north China market, utilizing data spanning the time period of 08/23/2013–04/15/2021. Predictions of this essential commodity price signal have not received adequate consideration in previous studies. Price predictions are produced here through Gaussian process regression approaches that are constructed via cross-validation procedures with Bayesian optimization techniques. With a 0.1325% relative root mean square error, the models produce rather accurate projections of prices throughout the out-of-sample testing timeframe spanning 09/17/2019–04/15/2021. Governments and investors might utilize price research models here to build informed judgments in the regional scrap steel market.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it