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
Purpose: To compare symbolic regression by genetic programming (SRGP) with symbolic regression by random search (SRRS), a novel method for symbolic regression described herein. Methods: We limit our problem space to N binary trees, m terminals and n functions, then use a dense enumeration of full binary trees to perform uniform random sampling from the set of all permitted equations. We compare a single basic configuration of symbolic regression by genetic programming with symbolic regression by random search using 1000 randomly generated problems. We perform a hyperparameter search with 50 randomly generated symbolic regression problems and 198 randomly generated hyperparameter configurations, examining the performance of SRGP against SRRS. Results: For the single configuration experiment, SRGP outperformed SRRS in 49.0% of problems, random search was best in 26.2% of problems, and there was a tie in 24.8% of problems. Of the cases that were not tied, genetic programming was best in 65.6% of experiments (99% CI, [60.7%, 69.2%]). Of the cases that were not tied in the hyperparameter search, SRGP was best in 44% (99% CI, [41%, 48%]) of cases. The average random configuration of SRGP performs worse than does SRRS.
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.
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 | 0.000 | 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 | 0.002 |
| 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