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Record W3133996682 · doi:10.1017/s0957423920000089

ʾABŪ SAʿĪD AL-SIǦZĪ AND THE “STRUCTURE OF THE ORBS,” THE EARLIEST KNOWN WORK ON <i>HAYʾA</i>

2021· article· en· W3133996682 on OpenAlexfundno aff
Younes Mahdavi

Bibliographic record

VenueArabic Sciences and Philosophy · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHistorical Astronomy and Related Studies
Canadian institutionsnot available
FundersUniversity of TehranMcGill UniversityUniversity of Oklahoma
KeywordsAstronomerHayIndependence (probability theory)NinthPeriod (music)BiographyPhysicsPhilosophyArtLiteratureArt historyMathematicsStatisticsAnimal science

Abstract

fetched live from OpenAlex

Abstract ʾAbū Saʿīd al-Siǧzī was a prominent fourth/tenth century astronomer and mathematician who was one of the first contributors to the new genre of ʿilm al-hay ʾa (science of the configuration). However, little is known about the initial steps taken in the formation of the discipline, or its independence from other astronomical writings and practices. In this paper, I will discuss new findings about Siǧzī’s life to determine details of his biography and a more precise time period for his scientific activities. I then describe, for the first time, a composition in theoretical astronomy from the fourth/tenth century, the “Structure of the orbs” ( Tarkīb al-aflāk ) by Siǧzī to show its place in the formation of the discipline of ʿilm al-hay ʾa , comparing Siǧzī’s book to the earlier work of al-Farġānī on the size of the earth and the number of celestial spheres. I conclude that Siǧzī’s Tarkīb al-aflāk is the earliest known example of a book that contains only topics found in later hay ʾa and may be the first appearance of the ninth celestial orb that became standard in the later genre.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.013
Scholarly communication0.0030.004
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2021
Admission routes1
Has abstractyes

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