A Psychometric Approach to Edge Detector Calibration in Grey-scale Images
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
An edge detection algorithm is a filter which significantly reduces the amount of information present in an image such that only high frequency changes in either range or intensity are visible in the resulting image. In order to perform effective edge detection the user must have a clear idea of the frequency above which an edge will be identified. In grey-scale images, edges represent sudden or high-frequency changes in the grey-scale, also referred to as intensity or luminence, level of an image. In practice, what is considered a "high-frequency change" is dependent upon the purpose for which the edge detector has been selected. In this paper it is proposed that to determine the minimum grey-level threshold and per-pixel intensity change at which the user deems a "true" step edge exists, psychometric testing is required. This information is then used to calibrate common edge detection methods which are subsequently used to filter a series of common grey-scale images
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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