PENGELOMPOKAN PERLOMBAAN KSN (KOMPETISI SAINS NASIONAL) JENJANG SMP BERDASARKAN CABANG LOMBA MENGGUNAKAN METODE CLUSTERING (STUDI KASUS : DINAS PENDIDIKAN KAB.LANGKAT)
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
To get the KSN competition (National Science competition) grouping application contained in the data archive, the Education Office needs to have a competition grouping data system that has a structured and clear procedure that is in accordance with the vision, mission and strategy. Because in the KSN data competition that was taking place, data was only inputted manually. So here I want to make a Grouping Application to see the grouping data of the KSN competition based on the competition branch, school and district origin, it is necessary to do the application for the design of the KSN competition grouping data system. Tests carried out using the clustering method with the K-Means algorithm, it can be seen that the KSN competition group, the competition branch, from the school and the sub-district which only has the highest group and appears most frequently in the KSN competition grouping.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it