<i>Herschel</i>PEP/HerMES: the redshift evolution (0 ≤<i>z</i>≤ 4) of dust attenuation and of the total (UV+IR) star formation rate density
Bibliographic record
Abstract
Using new homogeneous luminosity functions (LFs) in the far-ultraviolet (FUV) from VVDS and in the far-infrared (FIR) from Herschel/PEP and Herschel/HerMES, we studied the evolution of the dust attenuation with redshift. With this information, we were able to estimate the redshift evolution of the total (FUV + FIR) star formation rate density (SFRDTOT). By integrating SFRDTOT, we followed the mass building and analyzed the redshift evolution of the stellar mass density (SMD). This article aims at providing a complete view of star formation from the local Universe to z ~ 4 and, using assumptions on earlier star formation history, compares this evolution with previously published data in an attempt to draw a homogeneous picture of the global evolution of star formation in galaxies. Our main conclusions are that: 1) the dust attenuation AFUV is found to increase from z = 0 to z ~ 1.2 and then starts to decrease until our last data point at z = 3.6; 2) the estimated SFRD confirms published results to z ~ 2. At z > 2, we observe either a plateau or a small increase up to z ~ 3 and then a likely decrease up to z = 3.6; 3) the peak of AFUV is delayed with respect to the plateau of SFRDTOT and a probable origin might be found in the evolution of the bright ends of the FUV and FIR LFs; 4) using assumptions (exponential rise and linear rise with time) for the evolution of the star formation density from z = 3.6 to zform = 10, we integrated SFRDTOT and obtained a good agreement with the published SMDs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".