Morphological Analysis of H<scp>i</scp>Features. I. Metric Space Technique
Bibliographic record
Abstract
This is the first of two papers on the morphological analysis of H I features. In this first paper, we use the so-called metric space technique, developed by F. C. Adams and J. Wiseman. The metric space technique is an image analysis, mathematical formalism used to quantitatively compare astrophysical maps according to complexity. Instead of comparing maps on a pixel-by-pixel basis, we compare the maps' one-dimensional "output functions," which characterize specific morphological/physical aspects of the maps. The tool is used to analyze 28 H I features of known origin taken from the Canadian Galactic Plane Survey (CGPS), where the maps are scaled at 18'' per pixel (resolution of 1 cos δ arcmin). Technical and mathematical improvements to the formalism are presented. After classifying the 28 maps according to complexity, we searched for correlations between this complexity ranking and other quantifiable aspects of the H I features such as age, area, H I area, distance, flux from the ionizing star(s), fractal dimension, H I mass, and | z | (the absolute value of the height of the objects, above or below the Galactic plane). The most interesting correlations are (1) the higher the flux of UV photons, the more complex is the photodissociated H I feature, and (2) the older the supernova remnant, the more complex the H I associated with it. There is no correlation between the fractal dimension of the maps and their complexity or their physical characteristics, thus showing that the metric space technique could be used as a solution to the degeneracy of the fractal dimension.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".