Bibliographic record
Abstract
The author had been focusing on various treatments of time in physics for decades. The author felt that there were major shortcomings in the treatment of time in relativity and even in Newtonian physics. Then the author took a very pragmatic look at how time was actually treated in GPS to achieve a high precision, real world, working, physics system. The author thought that the actual GPS approach might yield a pragmatic time construct for physics. GPS does NOT use special relativity’s construct of time—but rather an observer independent construct as detailed below. The author put “Philosophical Time” musings aside and focused solely on finding the right physics construct for time. Finding that proper time was all that physics required, it dawned on the author, quite to his surprise, that proper time also met all the needs of philosophical time. Furthermore, an even deeper physics insight was that “proper time” is simply a measure of a stable physical process’ cycles. Hence, “time” as a separate physical entity disappeared to be replaced by physical processes each with its own idiosyncratic length/duration, all of which can be related to each other. These cyclic and noncyclic processes are all driven by known physical forces and energy sources. The properties of time, except for dimensionality, are actually just properties of each individual process that unfolds within 3D space and time is an emergent, human, generalization of process lengths.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".