THE CAVITY PROJECT: SPATIALLY-RESOLVED AND CHARACTERISTIC PROPERTIES OF GALAXIES DERIVED USING pyPipe3D
Bibliographic record
Abstract
We present the analysis using pyPipe3D of a sample of 208 galaxies from the CAVITY project , that includes: (i) a description of the processes performed by this pipeline, (ii) the data model adopted to store the spatially resolved properties, and (iii) a catalog comprising integrated and characteristics properties, and the slope of radial gradients for various observational and physical parameters determined for each galaxy. We elucidate the analysis outcomes by (i) presenting the spatial distribution of various derived parameters for a representative galaxy, CAVITY66239, and (ii) exploring the integrated extensive and intensive scaling relations that rule star-formation for this galaxy sample, comparing with results from the literature. The individual galaxy data products for the galaxies featured in the inaugural data release of the CAVITY project, along with the catalog described in this article, are available at the 1st Data Release web page.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".