Bibliographic record
Abstract
In this research, the facts of Pāzand and the rules of Pāzand writing will be examined. According to the findings of this research, the texts of Pāzand written by Pārsī Pāzand writers in India are in Pārsī Gujarātī language and the rules of Pāzand are completely related to the rules that exist in pārsī Gujarātī language. In the texts of Pāzand written by Pārsī Pāzand writers, the rules of Gujarātī language and dialectal rules of Pārsī Gujarātī language have been observed. In this research, all the rules are given with examples and examined and analyzed. Also, examples of words that exist in pārsī Gujarātī language are given from the first five chapters of the text of Shikand Gumānīk Vichār in order to become more familiar with the rules and types of words in this language. The author of this article, who is fully acquainted with Hindī, Urdū and Gujarātī languages, has found and studied all these words in these languages. This article is the result of a discovery made for the first time in the world by its author.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".