Bibliographic record
Abstract
We look at the impact of infrared (IR) and radio point sources on upcoming large-yield Sunyaev-Zel'dovich (SZ) cluster surveys such as those to be undertaken by the Atacama Pathfinder Experiment (APEX), the South Pole Telescope (SPT), and the Atacama Cosmology Telescope (ACT). The IR and radio point-source counts are based on observations by the Submillimeter Common-User Bolometric Array (SCUBA) and the Wilkinson Microwave Anisotropy Probe ( WMAP ) instruments, respectively. We show that the contributions from IR-source counts, when extrapolated from the SCUBA frequency of 350 GHz to the operating frequencies of these surveys (~100-300 GHz), can be a significant source of additional noise, which needs to be accounted for in order to extract the optimal science from these surveys. These surveys give us an opportunity to study IR sources, their numbers and clustering properties, opening a new window to the high-redshift universe. For the radio point sources, the contribution depends on a more uncertain extrapolation from 40 GHz, but is comparable to the IR near 200 GHz. However, the radio signal may be correlated with clusters of galaxies and have a disproportionately larger effect.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".