Bibliographic record
Abstract
Galaxy classification catalogs in the paper <em>Galaxy Spin Classification I: Z-wise vs S-wise Spirals With Chirality Equivariant Residual Network</em>. <strong>reduced_gz1.csv</strong> (173,097 galaxies) catalog column p_cw_gz, p_acw_gz n_vote p_cw_sdss, p_acw_sdss p_cw_desi, p_acw_desi description vote fractions from the original Galaxy Zoo 1 catalog total number of votes in Galaxy Zoo 1 CE-ResNet predictions using SDSS images CE-ResNet predictions using DESI images The remaining columns are from the SDSS SQL database. Some of the columns are renamed and you can find the original names below. Please visit the SDSS site for the description of the columns. <em>SDSS DR16 PhotoObj, https://skyserver.sdss.org/dr16/en/help/browser/browser.aspx?cmd=description+PhotoObjAll+U#&&history=description+PhotoObjAll+U</em> catalog column objid_16 ra dec rad_r rad_err_r r50_r original name objID ra dec PetroRad_r PetroRadErr_r petroR50_r catalog column r50_err_r r90_r r90_err_r m_u m_err_u m_g original name petroR50Err_r petroR90_r petroR90Err_r u err_u g catalog column m_err_g m_r m_err_r m_i m_err_i m_z original name err_g r err_r i err_i z catalog column m_err_z q_r q_err_r u_r u_err_r original name err_z q_r q_err_r u_r u_err_r <em>SDSS DR16 SpecObj, http://skyserver.sdss.org/dr16/en/help/browser/browser.aspx?cmd=description+SpecObjAll+U#&&history=description+SpecObjAll+U</em> catalog column z_s ra_s dec_s original name z ra dec <em>SDSS DR7 PhotoObj, </em>http://cas.sdss.org/dr7/en/help/browser/description.asp?n=PhotoObjAll&t=U catalog column objid_7 original name objID <strong>pre_desi.fits</strong> (1,953,246 galaxies) All the columns other than P_CW and P_ACW are from the DESI sweep catalogs. See https://www.legacysurvey.org/dr9/files/#sweep-catalogs-region-sweep.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.231 | 0.373 |
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".