Komposisi Musik "Warna" Memahami Warna Melalui Pendekatan Komposisi musik
Bibliographic record
Abstract
Komposisi musik Warna merupakan komposisi yang menggambarkan kesan rasa dan inspirasi yang d iberikan oleh wama. Di tengah kehidupan yang sulit di jaman sekarang, manusia membutuhkan inspirasi. Warna merupakan sesuatu yang mampu memberikan inspirasi dan wama memiliki keu nggulan bahwa setiap saat ia dilihat oleh manusia. Dengan pendekatan musikal, kesan rasa dan inspirasi itu dipertegas dan dipe1:jelas karen a bunyi lebih kuat daripada warna. Komposisi ini terdiri dari lima bagian pokok yaitu merah, biru, hijau, hitam , dan kuning. Merah adalah tentang keberan ian dan semangat. Biru adalah tentang langit dan doa yang beru lang. Hijau adalah tentang inspirasi untuk menjaga hijaunya bumi. Hitam adalah tentang kekacauan dan keheningan. Kuning adalah tentang \nkeceriaan dan memandang kesedihan sebagai bagian dari rancangan keceriaan yang besar. \nInspirasi-inspirasi itulah yang ingin dipertegas dalam karya komposisi ini.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.018 |
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".