Gaussian Process Regression (GPR) Model Development for Predicting the PEMFC Performance Against Temporal Hydrogen Crossover in Matlab
Bibliographic record
Abstract
This work focused on developing a Physics-informed ML model using Gaussian Process Regression (GPR) in predicting the open-circuit voltage (OCV) against temporal hydrogen crossover (HCO) current by employing the Shapley value analysis to explain the model predictions. First, the GPRbased model developed was seen to exceptionally perform well from model training to deployment based on the fit results(RMSE = 6.78E-05,R2 = 1.0000), correlation analysis (R = 1.0000), and statistical validation (p-value = 0.3216).The uncertainty range, as part of the results of a GPR-based model, suggests that the probability that the model predictions represent the actual OCV of the unseen data is high.Second, the global model interpretation suggested that both the HCO and time have strong influence on the OCV values although a positive impact was observed based on the direction of influence given by the Shapley summary which subjects the data used to test the Shapley algorithm to ambiguity.Anyhow, this finding was eventually contrasted as the Shapley dependence implied that majority of the Shapley values were observed under the zerovalue Shapley, indicating that both predictors negatively impacted the OCV.Lastly, the local Shapley inspection suggested that predictors have weak influence over the OCV at around 40,000 to 60,000 hours where great decline in the OCV values were recorded.HCO greatly dominated the OCV decline at the near end of the AST program.
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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 | 0.000 | 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 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".