Data Analysis and Knowledge Mining of Machine Learning in Soil Corrosion Factors of the Pipeline Safety
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
The purpose of this research is to enhance the ability of data analysis and knowledge mining in soil corrosion factors of the pipeline. According to its multifactor characteristics, the rough set algorithm is directly used to analyze and process the observation data without considering any prior information. We apply rough set algorithm to delete the duplicate same information and redundant items and simplify the condition attributes and decision indicators from the decision table. Combined with the simplified index, the decision tree method is used to analyze the root node and branch node of it, and the knowledge decision model is constructed. With the Python machine learning language and PyCharm Community Edition software, the algorithm functions of rough set and decision tree are realized, so as to carry out artificial intelligence analysis and judgment of the soil corrosion factor data in pipeline. Taking the area of loam soil corrosion as an example, the data analysis and knowledge mining of its multifactors original data are carried out through the model. The example verifies that the evaluation and classification rules of the model meet the requirements, and there are no problems such as inconsistency and heterogeneity. It provides decision-making service and theoretical basis for the soil corrosion management of pipeline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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