Study on Inversion of Damage Incentives of High Pile Wharf in Inland River Based on SEResNet
Bibliographic record
Abstract
Based on SEResNet neural network algorithm, the inversion model of damage incentives of inland high-piled wharf is constructed. The stress data of pile foundation under the action of damage incentives of high-piled wharf are obtained by using solid element finite element model calculation and indoor model test methods. The parameterized finite element calculation model of high-piled wharf is established by using subprocess in Python program to call MANSYS module, and verified with solid element model. The parameterized simplified finite element model meets the needs of inversion calculation. Based on the stress data samples of the pile foundation of the high-piled wharf obtained from the model test, the inversion analysis of single and multiple damage incentives is carried out. The model can identify the location, size and type of injury causative agent with good generalization ability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".