Edge detection of petrographic images using genetic programming
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
This paper discusses work in progress that uses genetic programming to evolve edge detectors for petrographic images. Microscopic images of thin sections from mineral samples are obtained using a rotating polarizer microscope. These images are then processed using a number of filters, resulting in a set of nine filtered image parameters. In order to be useful for higher--level analysis, such as automatic mineral identification, the grain boundaries within these images must be identified. Using genetic programming, edge detecting functions are evolved for this purpose. The edge detectors may use as any of the filtered image parameters as input. Since the source images are large, a subset of the images is sampled for training, and the remainder of the image is used for testing. This training data is selected with a biased random sampling strategy. The complexity of the images dictates that a generic edge detector for all mineral specimens is infeasible. Rather, the ...
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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