Bibliographic record
Abstract
The compilation of Urdu 'Nisab Name was started in the 16th century AD inspired by Persian Nisab Namé. In view of the specific number of poems in them, it is also called Manzoom Dictionaries. In these courses, Urdu synonym of words in Arabic and Persian languages, which are commonly used in everyday conversation, were included.Khaliq Bari has gotten so much importance in this regard. In Urdu language later on, many dictionaries were written in the style of Khaliq Bari, including Qasida Dar lughat e Hindi, Allah Khudai, Wahid Bari, Allah Bari, Samad Bari, Nisab e Ujab, Khushal ul-Sibyan, Mufid-ul-Bah's, Qadir Nama e Ghalib and The Anglo-Oriental Khaiq Bari. Special importance was given to the words of the Holy Qur'an and their Urdu and Persian interchangeable words were provided in this series of Dictionaries. Later, these words were not only used in Urdu poetry and prose, but also became the basis of their common practice. This article consists of a linguistic study of the same Qur'anic words.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.977 | 0.980 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".