Bibliographic record
Abstract
Sheikh Maḥmūd Saīd Mamdūḥ was expert in many departments of religious and Islamic studies. Although he got his master’s degree in agriculture as well as get Islamic education by many scholars of Arab. He wrote many books. Introduction of few books is as under. His famous book al-Tarīf in which he tried to explain the authenticity of Ḥadīths. In this book 846 Hadiths declared Ṣaḥīḥ by him. His book Bashārāt al Mūmin is to explain accuracy of a Ḥadith اتقوا فراسۃالمومن فانہ ینظر بنور اللہ. Tay al-Qirtas is about Ahl-e-Baīt. He collected Hadiths about family of Prophet (ﷺ). Tojīḥ al Aaīma is his criticism to a collection of Fatāwā. Which were published from Saʾudī Arabīa. A book al-Taqīb Al Amjad is explanation of من لغافلا جمعۃلہ. Tazīn Al Alfāẓ is biography of Ḥufāẓ-e-Ḥadīth. There are 19 biographies in this book. Al Qawl Al Mastaofī is about explanation of Ḥadith من خرج من بیتہ الی الصلاۃ. Al Itijāḥāt al-Ḥadīthīa is biography of scholars of fourteenth century AH. Al-Ālām is criticism of a Fatwā of Ibn-e-Taimyyah.
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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) | 0.002 | 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 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.984 | 0.989 |
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".