QF-LCA Dataset for Quantum Double-field Model, Game and Application
Bibliographic record
Abstract
Online mathematical tools from Ref. [94] in the paper were used to conduct computations to validate the data generated from the QDF model and evaluate the fmv (four main variables) equations. In addition, the method paper Ref. [7] for MethodsX J, implements and further validates the QDF model proving its universality for a set of CPTs and QPTs observed in a thermodynamic system. This includes the summary of the design and quantum thermodynamic aspects to implement and validate the "Quantum Field Lens Coding and Thermodynamic Metrics" algorithm (model) as a supplementary file to this paper. The method article as the quantum algorithm code evaluates the entanglement entropy of this model. Secondly, the QDF game demo and relevant data are available in this dataset. For instance, animating the data used in the communication steps 1-7 of the QDF system model in the article is presented via "QDFGTAnim.pptx" and "QDFGTAlgorithm.nb" files. Lastly, expert highlights of peer-reviewed QDF Game Theory aspects expanding to the universality of the model is part of this dataset as "Expert_Model Highlights (2022).pdf" document with discussion on how this model's universality was achieved in the current article's version throughout its QDF examples and proof of its theory.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.029 |
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".