Bibliographic record
Abstract
Since the 2016, STMIK AKAKOM Library implements the information system thoroughly by using developed application that should connect to other library application. Various menu that provided is Home, Kontak, Tautan, Layanan, Profile, Katalog Online, and Digital Library. Research aims to determine the pattern of visits to the web and identify what pages are frequently visited by visitors Research begins with literature study of a relevant topic, configure the Nginx server, collecting log access data during a certain time, until prepared them so the result and conclusions can be achieve. In web usage mining implementation there are three stages that are carried out to get libraries and sources of information namely preprocessing, pattern discovery and pattern analysis. The research object is the STMIK AKAKOM library site. The results show which pages that most frequently visited is ‘Home’, ‘Berita’, ‘Digital Library’, ‘Koleksi’, Tautan’, ‘Layanan’, ‘Katalog Online’, dan ‘Kontak’. The operating system or browser that used much more is Windows, Android, Linux, and iphone. By Internet Protocol, the most visitors came in is from outside of Akakom
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".