Bibliographic record
Abstract
To automate source detection, two-dimensional light profile Sérsic modelling and catalogue compilation in large survey applications, we introduce a new code Galaxy Analysis over Large Areas: Parameter Assessment by galfitting Objects from SExtractor (galapagos). Based on a single set-up, galapagos can process a complete set of survey images. It detects sources in the data, estimates a local sky background, cuts postage stamp images for all sources, prepares object masks, performs Sérsic fitting including neighbours and compiles all objects in a final output catalogue. For the initial source detection, galapagos applies SExtractor, while galfit is incorporated for modelling Sérsic profiles. It measures the background sky involved in the Sérsic fitting by means of a flux growth curve. galapagos determines postage stamp sizes based on SExtractor shape parameters. In order to obtain precise model parameters, galapagos incorporates a complex sorting mechanism and makes use of modern CPU’s multiplexing capabilities. It combines SExtractor and galfit data in a single output table. When incorporating information from overlapping tiles, galapagos automatically removes multiple entries from identical sources. galapagos is programmed in the Interactive Data Language (idl). We test the stability and the ability to properly recover structural parameters extensively with artificial image simulations. Moreover, we apply galapagos successfully to the STAGES data set. For one-orbit Hubble Space Telescope data, a single 2.2-GHz CPU processes about 1000 primary sources per 24 h. Note that galapagos results depend critically on the user-defined parameter set-up. This paper provides useful guidelines to help the user make sensible choices.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".