Method for Calculating Text Similarity Of Cross-Weighted Products Applied To Power Grid Model Search
Bibliographic record
Abstract
the ranking of search results in grid model search uses the method of ranking by comprehensive score from high to low. The comprehensive score is calculated by multi-field comprehensive text similarity score, filter matching score, and attention score, calculated according to a certain percentage. The basis of the multi-field comprehensive text similarity algorithm is the similarity calculation method of short text, which needs to be flexibly adjusted according to the characteristics of various fields of data in the grid model. Therefore, a short text similarity calculation method with certain adjustability is designed. The algorithm constructs two weight arrays with the same length as the two short text that need to be calculated for similarity and assigns the initial weight value, then traverses the characters in one of the strings, and adjusts their weight according to whether they exist in another, then calculates the weight cross product of single word matching and continuous matching characters to obtain the text similarity weight, and obtains the text similarity value by dividing the product of the two original text total weights. The result of grid model search based on the cross similarity algorithm is closer to the expectation of power system users in terms of the accuracy of search results.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".