Dynamic Universe Model’s Prediction “No Dark Matter” in the Universe Came True!
Bibliographic record
Abstract
This paper discusses about Dark matter or Missing mass in Galaxies. In this work the tensor mathematics called Dynamic Universe Model was used to find out theoretical star circular velocity curves in a Galaxy. Here we are presenting four main cases. In the first case, there is a Galaxy with huge central mass at the center, sun like stars and external galaxies are present in the calculations; only in this case the theoretical predictions of circular velocity curves (star circular velocity verses star distance from the center of galaxy) were matching with the observed velocities. In the later three cases either the huge central mass was absent or external galaxies were absent or both were absent, the theoretical circular velocities did not match the observations. Hence the question of missing mass / dark matter does not arise as it is only a calculations error. This prediction was first presented in Tokyo University in 2005, that No dark matter (Missing mass) is required according to Dynamic Universe Model. Later the findings from LUX in 2013 the (Large Underground Xenon) experiment confirmed this prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".