SISTEM APLIKASI UJIAN BERBASIS LOCAL AREA NETWORK (LAN) PADA SMA SWAKARYA BINJAI
Bibliographic record
Abstract
One of the routine activities undertaken to as an evaluation tool to assess how much ability and understanding and skills that have been obtained by the students is to conduct exams. In Conventional , exam requires paper stationery items and devices. In addition , the conventional exam vulnerable to fraud and requires teachers to correct students' answers . For reasons of efficiency , it can be built by using the online exam application Local Area Network ( LAN ) . The application consists of two , namely the application server ( for administrators and teachers ) and the client (for students ) . Under this system , admin ( head or deputy principals ) determine the schedule of exams and teachers to enter the exam through an application server . Then , the students answer exam questions through the client application . Problem is sent from the application server to the client , the first randomized sequence, so that each student will get about random sequence . Then , the students answer exam questions through the client application . Problem is sent from the application server to the client , the first randomized sequence, so that each student will get about random sequence .
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.027 |
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".