SPICA Deep Cosmological Survey: From AKARI to SPICA
Bibliographic record
Abstract
On the basis of the results of the AKARI far-infrared deep surveys, we propose a multi-wavelength far-infrared deep cosmological survey with SAFARI on SPICA. We have carried out a far-infrared deep cosmological survey with AKARI, and successfully obtained the galaxy counts and new limits on the absolute brightness of the cosmic far-infrared background. These results provide strong constraints on evolutionary scenarios, and suggest the necessity for a new model to explain galaxy evolution. Thanks to the excellent sensitivity and spatial resolution of SPICA/SAFARI, we will be able to resolve the cosmic infrared background into individual ob jects with 3 times or more higher spatial resolution than that of AKARI, and will also be able to conduct infrared photometry and spectroscopy on those ob jects. The far-infrared deep survey with SPICA/SAFARI will be an ideal opportunity to reveal the origin of the cosmic far-infrared background residual brightness and fluctuations. These observation will allow us to reveal the star formation history in the early Universe without the uncertainty of dust attenuation, which is essential if we are to understand the process of galaxy formation.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".