The Spitzer Extragalactic Representative Volume Survey (SERVS): Survey Definition and Goals*
Bibliographic record
Abstract
We present the Spitzer Extragalactic Representative Volume Survey (SERVS), an 18 deg 2 medium-deep survey at 3.6 and 4.5 μm with the postcryogenic Spitzer Space Telescope to ≈2 μJy (AB = 23.1) depth of five highly observed astronomical fields (ELAIS-N1, ELAIS-S1, Lockman Hole, Chandra Deep Field South, and XMM-LSS). SERVS is designed to enable the study of galaxy evolution as a function of environment from z ∼ 5 to the present day and is the first extragalactic survey that is both large enough and deep enough to put rare objects such as luminous quasars and galaxy clusters at z ≳ 1 into their cosmological context. SERVS is designed to overlap with several key surveys at optical, near- through far-infrared, submillimeter, and radio wavelengths to provide an unprecedented view of the formation and evolution of massive galaxies. In this article, we discuss the SERVS survey design, the data processing flow from image reduction and mosaicking to catalogs, and coverage of ancillary data from other surveys in the SERVS fields. We also highlight a variety of early science results from the survey.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".