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Record W4411390450 · doi:10.53762/j17hzy81

10.53762/j17hzy81

2000· article· en· W4411390450 on OpenAlexvenueno aff
Hafiza Nasreen Akhtar, Iftikhar Khan

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.592
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.9940.981

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.

Opus teacher head0.006
GPT teacher head0.211
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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