Investigating Decoupage and Cinematic Special-Effects in Ferdowsi's Shahnameh
Bibliographic record
Abstract
Decoupage or cinematic segmentation is a step by step map used by filmmaker to produce a film or television show, and cinematic special effect is reconstruction and re-creation of unrealistic or impossible scenes during production process. Shahnameh Ferdowsi, due to its attractive, deep and imaginative stories, enjoys a high capacity to be exploited in the field of movies and cinematic productions. The author aims to define principles and basic concepts of decoupage and special effects as well as to extract famous stories on each of the mythological, heroic and historical sections of the Shahnameh and to adapt the poems to cinematic segmentations. The author also aims to investigate each hemistich or line as a visual shot, and battles descriptions and marvels illustrated in epic and heroic works as special effects which are capable of being reconstructed in the world of movies. Analysis of mentioned stories and adapting Ferdowsi's imageries to the scenes defined in the modern scripts, has flaunted inherent ability of the Shahnameh to regenerate and adapt itself to the present time due to its creative and illustrator poet, and represents Ferdowsi as a filmmaker with a cinematic mind. At the end, the story will be analyzed and decoupaged and turns into a full screenplay, without any interferes and just based on Ferdowsi poems to become a work of art in various visual formats such as movies, TV shows, animations, computer games, etc. At the same time, it accounts for both aspects of form and content in a prominent work.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".