Visi Pustaka Jaringan Informasi antar Perpustakaan 7 artikel Vol. 18, no. 1, april2016
Bibliographic record
Abstract
1. Perpustakaan Data: Sebuah Pengamatan Terhadap University of Toronto Map Data library.2. Evaluasi Ketersediaan Artikel jurnal Ases Terbuka untuk Cabang Ilmu Minyak dan Gas Bumi.3. analisis Pemaknaan Pemustaka Atas Ruang Perpustakaan FEB Undip.4. literasi Internet Petani Wilayah Persen Tegaldlimo dalam Rangka Implementasi Sawah Digital di kabupaten Banyuwangi Jawa Timur.5. Cerita Dongeng di Pusaran Arus Digital: membangun Koleksi Film Animasi Digital di Perpustakaan sebagai Upaya Mengembangkan Jiwa Nasionalisme.6. Tantangan Perpustakaan dan Arsip untuk Memilih Metode Mass Deasidifikasi Sebagai Salah Satu Upaya Pelestarian Dalam Menyelamatkan bahan Perpustakaan dari Kehancuran.7. Kajian Permintaan Standar Nasional Indonesia ( SNI ) Melalui PNPB d Perpustakaan BSN.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.517 | 0.320 |
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".