Multi‐source domains transfer learning strategy based on similarity measurement for batch process quality prediction
Bibliographic record
Abstract
Abstract Generally, when multiple source processes are available for transfer modelling, an inappropriate transfer learning strategy may lead to the deterioration of model accuracy and even cause negative transfer. For this issue, a multi‐source domains transfer learning strategy based on similarity measurement is proposed in this work for selecting the optimal transfer modelling strategy according to different scenarios. By counting the data amount and judging the similarity between each source domain and the target domain, the rich information contained in multiple source processes is well utilized to assist the modelling process in the target domain. The proposed strategy provides three alternative specific transfer mechanisms by setting the judgement conditions so that the possibility of negative transfer can get reduced and the accuracy of prediction can be guaranteed. To ensure the reliability of the transfer model and adapt to the change in conditions, the updating scheme based on online data is adopted. Finally, the mixed‐effects Gaussian processes modelling method is taken as an example to verify the effectiveness of the proposed multi‐source transfer learning strategy through the simulation experiment of the cobalt oxalate synthesis process.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".