Bibliographic record
Abstract
One of the best legal science is to engage in the words of Allah revealed to Hazrat Muhammad in clarifying, explaining its meaning ruling significances and words. This science is called the science of interpretation. The interpreter's approaches varied in interpreting the holy Quran, some of them follow the common methodology such as Mathur, Language and opinions such as al-Tebari. There is no doubt that the jurisprudence interpretation of the holy Quran has a great value, so sharia scholars has divided the topics of the holy Quran in three categories. The first section is related to the belief in monotheism, or religion the second section is related to the morals and the third one deals with the practical provisions and these provisions are extracted from the holy Quran this is called the jurisprudence of Quran or the provisions of the Quran. A person can not become a complete Muslim with out knowing this because people need them in their worships, dealings and activities of the life. Looking at this importance, scholars before and after studied these verses under a specific manner they called it"Ahkam ul Quran" Among the scholars, Allah chose a scholar named zainuddin ibni nujaim to serve his glorious book, in fact he was a great jurist. He was born in the city of Qahira in the year 926 Hijra. He was a model for the jurists, an Imam in matters of religion. That great scholar and personality died on Wednesday corresponding to the 8th Rajab in the year 970 Hijra.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.982 | 0.987 |
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".