Evaluation of Scientific Thinking of Zakaria Razi about the World of Natural and Cosmology
Bibliographic record
Abstract
In the fourth and fifth century of AH (tenth and eleventh. m) all science, especially natural science and cosmology were interest to people particular to scientists. These centuries was in a row of the best periods of prosperity, science and culture and became famous in the history and civilization of "the golden age of Islamic Iran". As a result, new scientific findings add to the scientific achievements of the past and also influenced the minds of scientists in the later centuries. Razi had a big contribution in the development of philosophy and natural sciences at the beginning of the fourth century AH equal to the tenth century AD and achieve the highest degrees in their time in various fields. Razi raised the opinion of the ancient alchemy of Khamse, and also transformed elixir to chemistry. And was founded the core of chemistry and give chemistry to the medicine. among what was written in the third to seventh century of AD about race (the ninth to the thirteenth century), we find many cases that all of them express his accurate method in the clinical experimental method. Razi not only experiences in medicine but also knew tests as essential topics in the natural sciences. Human scientific past was known by thoughts of scientists such as Razi and their theories are the cornerstone of philosophers thinking of recent history. Therefore, this article seeks to examine the scientific thinking of Razi about the world of nature and cosmology. For this purpose, the method of library and tools of notes taking was used.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".