IMPLEMENTATION OF THE HAMMING CODE METHOD IN BIT DATA IMPROVEMENT TRANSMISSION PROCESS
Bibliographic record
Abstract
Dalam sistem komunikasi, keberhasilan penyampaian informasi dari pengirim (transmitter) kepada penerima (receiver) tergantung pada seberapa akurat penerima dapat menerima sinyal yang ditransmisikan dengan baik dan benar. Nyatanya sinyal informasi yang diterima masih banyak terdapat kesalahan sehingga diperoleh data corrupt (bit error) yang disebabkan oleh noise (sinyal pengganggu) ketika proses pengiriman data sehingga menyebabkan file tersebut tidak bisa dibaca. Maka dari itu diperlukan teknologi untuk memperbaiki kesalahan pada bit error tersebut, yaitu menggunakan metode hamming code. Hamming code merupakan salah satu jenis linier error correcting code yang sederhana dan banyak digunakan pada peralatan elektronik. Metode hamming code bekerja dengan menyisipkan beberapa buah check bit ke data. Jumlah check bit yang di sisipkan tergantung pada panjang data. Hamming code menggunakan operasi Ex-OR (Exclusive OR) dalam proses pendeteksian maupun proses pengkoreksian error.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".