Bibliographic record
Abstract
Observation of a multifaceted mathematical-computational structure of Quran through analysis of its letter and word frequencies and important implications of such observations have been extensively explained and discussed in a recent article: “Khodadoost B. (2015) The Computed Scripture: Exponentially Based Fourier Regulated Construct of Quran and its fundamentally important Consequences". In the present article we report observation of yet another facet of this mathematical structure of Quran which is a phenomenal "parametric name-printing”. This observation has been made through a systematic compute-plot algorithm which uses the given name and chapter frequencies of letters in Quran as its input and shows in the output, calligraphic printing in Arabic of the same name. Several names of God, Major Prophets, and even some physicists are shown to clearly manifest these calligraphic effects. Sensitivities of these observations to changes in letter frequencies in Quran are so high that increase or decrease of even one letter and only in one chapter of Quran can completely demolish the calligraphic effects. These astonishing observations not only are extremely important and interesting in their own right, but also point to an immensely complicated and intricate super-intelligent mathematical design of Quran and reinforce "Mathematically Fully constrained Writing" or MFCW identity of this scripture and its consequences, as have been explained in the above article.
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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 | 0.006 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".