Development of experimental error-Driven model for prediction of corrosion rates of amines based on their chemical structures
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
This work investigated the relationships between amine corrosion rates and their chemical structural properties for application in the development of a Gaussian Process Regression (GPR) model for chemical structure-based prediction of corrosion rate of any amine. The GPR model accounted for experimental errors, which widened its scope to accurately predict the true corrosion rates, being restricted only to error associated with the trained model. The Average Absolute Deviation (AAD) between experimental corrosion rates and model predicted rates was 4.26 % for the test data, and 5.32 % for two test data unknown to the model. This showed that the model is generalizable and its predictions are accurate. This work also developed a user-friendly Graphical-User Interface, which allows a user to define any amine's structure to provide needed information to calculate its surface tension and steric effects for use as input variables to the model in predicting the corrosion rate of the amine.
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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 | 0.000 | 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 it