Bibliographic record
Abstract
Abstract Using the observed submillimetre source counts, from 250 to 1200μm[including the most recent 250, 350 and 500μm counts from Balloon-borne Large-Aperture Submillimetre Telescope (BLAST)], we present a model capable of reproducing these results, which is used as a basis to make predictions for upcoming surveys with the Spectral and Photometric Imaging Receive (SPIRE) instrument aboard the Herschel Space Observatory. The model successfully fits both the integral and differential source counts of submillimetre galaxies in all wavebands, predicting that while ultra-luminous infrared (IR) galaxies dominate at the brightest flux densities, the bulk of the IR background is due to the less luminous IR galaxy population. The model also predicts confusion limits and contributions to the cosmic IR background that are consistent with the BLAST results. Applying this to SPIRE gives predicted source confusion limits of 19.4, 20.5 and 16.1mJy in the 250, 350 and 500μm bands, respectively. This means the SPIRE surveys should achieve sensitivities 1.5 times deeper than the BLAST, revealing a fainter population of IR-luminous galaxies and detecting approximately 2600, 1300 and 700 sources per deg2 in the SPIRE bands (with one in three sources expected to be a high-redshift ultra-luminous source at 500μm). The model number redshift distributions predict a bimodal distribution of local quiescent galaxies and a high-redshift peak corresponding to strongly evolving star-forming galaxies. It suggests the very deepest surveys with Herschel–SPIRE ought to sample the source population responsible for the bulk of the IR background.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".