Bibliographic record
Abstract
The sub-continent is one of Allah’s blessed lands, it is the Land of preachers, Scholars, Religion people, Traders, writers and poets. Arabs were aware of the importance of Sub-continent, because they used to travel to Subcontinent for trade before Islam. When Muhammad bin Qasim came for the purpose of conquering India, many scholars, writers, and poets came with him, and they exerted their efforts to spread Islam and the Arabic language, most of whom were religious scholars and Sufis, and among the most famous Arabic poets in the Indian subcontinent, Like Abu Atta Al-Sindi, Al-Biruni, Ata bin Yaqoub Al-Ghaznawi, and Sheikh Fakhr Al-Din Al-Iraqi. Sheikh Rukn al-Din al-Multani, Judge Abd al-Muqtadir al-Sharihi al-Kindi, Sheikh Ahmad al-Thansiri, Shah Wali Allah Mohaddith al-Dahlawi, Sayyed Ghulam Ali Azad al-Bilgrami, Sheikh Muhammad Saeed al-Sindi, and others. Sheikh Muhammad Saeed Al-Sindhi was a popular religious scholar, a famous Sufi, and a great and talented poet. He demonstrated his poetic talent in the three languages: Arabic, Persian, and Sindhi. He wrote his poems in Arabic and Persian. Indian and Arab scholars have praised his Broad expertise in the Arabic language and literature. He writes many poems in these two languages, which became famous in the Indian subcontinent because of his expertise. We find many literary virtues in his poems, such as the diverse words, structures, and styles. Beautiful pictures, deep imagination, music, etc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.976 | 0.981 |
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".