Bibliographic record
Abstract
Three hundred years ago, Isaac Newton published a number of hypotheses on the structure of matter, which were ahead of their time by some two centuries. Speculations were made by Newton that may now be interpreted as precursors to fundamental elements of quantum mechanics and quantum field theory. General features of the layered structure of matter that is now known to exist in the form of nucleons, nuclei, atoms, molecules, and macromolecules were successfully predicted, and hypotheses on self-similarity, simplicity, and purpose were made. In this essay, Newton’s hypotheses are examined in the light of current understanding of matter at the subnucleonic scale. It is found that his hypotheses of self-similarity, simplicity, and purpose raise questions for the quarks and gluons of the current Standard Model (SM), but that various precursors to the SM are more compliant. Experimental tests of the precursors using the Large Hadron Collider and the proposed Large Hadron Electron Collider at CERN are described that could resolve the situation. In addition, it is suggested that Newton’s hypotheses could serve as the basis for the formulation of one or more “postulates of particle physics” comparable to the postulates on which Einstein based his theories of relativity a century ago.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".