EVALUATION OF FORECASTS PRODUCED BY GENETICALLY EVOLVED MODELS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Genetic programming (GP) is a random search computer algorithm that parallels Darwin’s theory of evolution and survival of the fittest. It finds application in pattern recognition and optimization problems in the natural sciences, engineering, business, and social sciences. This paper introduces GP and uses a GP computer program to evolve time-series models especially relevant for applied statisticians. Prediction models are evolved for simulated noise-free and noisy data as well as for real world Canadian lynx and sunspot numbers. Forecasts produced by the fittest of the genetically evolved models are evaluated and compared with available forecasts in prior studies. KEY WORDS: Time-series prediction; Computational methods; Nonlinear
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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 | 0.004 | 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it