Bibliographic record
Abstract
The Great Pyramid of Giza has fascinated us all as it encodes enormous amount of numerical coincidences such as dimensional precision, movement of our planet, speed of light, the golden ratio of Pi & Phi, etc.Studies have reasoned that the great pyramid of Giza has expressed the key ratio of an AC voltage sine wave as well as the ratios of Fibonacci number in developing the pyramidal design. Therefore in this study, the pyramid structure is considered as a resonator of waves where reflection of waves is an obvious phenomenon. The waves entering the pyramidal resonator will be reflected inward as they reflect from a curved surface according to the law of reflection. Since, a reflecting wave involves the energy-transport process, it determines our main objective to review and internalize the energy caused by reflection of the waves which occurs inside the pyramidal resonator. It is assumed that there is a strength point of such energy due to a higher volume of reflected waves to a single point. According to the law of reflection, when reflection occurs through a curved surface, it focuses incoming parallel waves to a convergence spot. This project is subjected to study the pyramid as a resonator of waves and aims to detect, observationally, the strength point of energy assumed to be caused by maximum number of reflected waves.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".