HerMES: COSMIC INFRARED BACKGROUND ANISOTROPIES AND THE CLUSTERING OF DUSTY STAR-FORMING GALAXIES
Bibliographic record
Abstract
Star formation is well traced by dust, which absorbs the \nUV/optical light produced by young stars in actively starforming \nregions and re-emits the energy in the far-infrared/ \nsubmillimeter (FIR/submm; e.g., Savage & Mathis 1979). \nRoughly half of all starlight ever produced has been reprocessed \nby dusty star-forming galaxies (DSFGs; e.g., Hauser & Dwek \n2001; Dole et al. 2006), and this emission is responsible \nfor the ubiquitous cosmic infrared background (CIB; Puget \net al. 1996; Fixsen et al. 1998). The mechanisms responsible \nfor the presence or absence of star formation are partially \ndependent on the local environment (e.g., major mergers: \nNarayanan et al. 2010; condensation or cold accretion: Dekel \net al. 2009, photoionization heating, supernovae, active galactic \nnuclei, and virial shocks: Birnboim & Dekel 2003; Granato \net al. 2004; Bower et al. 2006). Thus, the specifics of the \ngalaxy distribution—which can be determined statistically to \nhigh precision by measuring their clustering properties—inform \nthe relationship of star formation and dark matter density, and \nare valuable inputs for models of galaxy formation. However, \nmeasuring the clustering of DSFGs has historically proven \ndifficult to do.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".