Position of Birds in Descriptive - Visual Language of Rudaki
Bibliographic record
Abstract
Abu Abdollah Jafar ibn Mohammad Rudaki, the founder father of Persian poetry Khorasani style. The remainder of his poems about eight hundred and four bits and the rest of his poetry is gone. Rudaki's poetry like odes, quatrains, Masnavi, had a piece of lyric skills. By studying the debris of Rudaki, a collection of his thoughts and feelings we find that his poetic mind in the realm of nature as a source of natural elements in the arte ries that feed blood flows like poetry images as image, concept, dynamic and animated poetry and his similes close to the mind, exciting and beautiful and novel. In this paper I have tried the birds that are a part of nature. In visual language, poet's descriptive video review. As the findings suggest that birds such as the nightingale in his poems name (Andalib, a thousand hands, Zondoaf), Baz, Cherz, Soveh (Tez), Zillah, peacock, cock, Asfour, owls, vultures, swallows, warblers, Quebec , eagle, starlings, Shakhish, moo (duck), P, chough, Cuckoo (Salsal), crow, chickens and picks to come. The birds sometimes described in the true sense, with regard to singing, the power and the ability to fly, power and beauty, ugliness, beauty and color was manifested in the artistic language poet and sometimes ironic form with the exaggerated praise, criticism or objections have been. Quranic verses and proverbs and also for the express purpose to warrant that kind of resort has been associated with birds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".