Bibliographic record
Abstract
In the history of exchanges between Islamicate and Sanskrit astral sciences, Nityānanda's Siddhāntasindhu (c. early 1630s), composed at the court of the Mughal emperor Shāh Jahān (r. 1628─58), is among the earliest examples of a Persian astronomical text translated into Sanskrit. In an earlier study, Misra (2021) described the sociohistorical context in which Nityānanda translated Mullā Farīd's Zīj-i Shāh Jahānī (c. 1629/30) into Sanskrit, and among other things, provided parallel comparative editions, with English translations, of the Persian and Sanskrit text describing the computation of true declination of a celestial object. While Misra's paper focused on the linguistic aspects of the translation process, the present paper studies the mathematics of the three methods of computing the true declination vis-à-vis Nityānanda's recension of his Sanskrit translations from his germinal Siddhāntasindhu to his chef d'œuvre, the Sarvasiddhāntarāja (1638). The paper begins by discussing the transformation of the Sanskrit text from the Siddhāntasindhu Part II.6 to the spaṣṭakrāntyadhikāra 'topic of true declination' in the gaṇitādhyāya 'chapter on computations' (henceforth identified as I.spa·krā) of his Sarvasiddhāntarāja. The metrical verses of Sarvasiddhāntarāja I.spa·krā are edited and translated into English for the very first time. A large part of this paper focuses on the technical (mathematical) analysis of the three methods of true declination, and includes detailed explanatory and historical notes. The paper also includes several technical appendices and an indexed glossary of technical terms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".