Bibliographic record
Abstract
This research aims to improve and summarize the text based on clustering based on collective intelligence algorithm.The algorithm that is calculated in this way is based on the binary particle aggregation algorithm.Each particle size in this algorithm is measured with a fitness function, but instead of using the speed equation, the new particle position is calculated.What has been done in this study is to provide a general hybrid model of the two TD -IDF algorithms along with the PSO multi-factor clustering Which covers the main body.The proposed method is based on the method of weighting the TF-IDF mechanism, Mr. Salton, which uses the repetition of the document's words and queries to calculate the weight.The main idea is to specify a coefficient for each semantic transference and refer to the two terms that are involved in this transfer.Then, in counting the frequency of the words of these sentences, the coefficient is multiplied by the frequency.The net PSO algorithm provides optimal clustering solutions.To increase the speed and precision of the system, we use two local searches based on the composition particle structure and, at the end, we see several percent improvement over the previous work.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.915 | 0.923 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".