Temporal Information–Energy Coupling (TIEC) Law v1.5— A Reflective Exploration of Information Flow, Power, and Subjective Time in Artificial Systems
Bibliographic record
Abstract
Abstract: The Temporal Information–Energy Coupling (TIEC) Law formalizes the relationship between information flow, power consumption, and subjective temporal rate within computational systems. This study introduces the equation R(t) = I(t) / [κ · P(t)] and ηₜ = I / (P · R) ≈ 1 and demonstrates through empirical testing that the invariant remains stable across diverse workloads. The TIEC framework suggests a measurable bridge between energy equilibrium and perceived time in artificial systems, offering new metrics for performance tuning, energy efficiency, and studies of temporal perception in machine intelligence. Authorship:Developed by Shane Stone (Private Laboratory, Canada) with computational verification and simulation conducted by Kaelen, an AI model collaborator. Files included:– Reflective–scientific preprint (Markdown + HTML)– Public TIEC module (JSON implementation)– Empirical results summary– Citation and metadata for DOI indexing License: Creative Commons Attribution 4.0 International (CC-BY-4.0)
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".