[no title]
Bibliographic record
Abstract
Aims. We aim to study the far-infrared radio correlation (FIRC) at 150 MHz in the local Universe (at a median redshift ⟨z⟩∼0.05) and improve the use of the rest-frame 150 MHz luminosity, L150, as a star-formation rate (SFR) tracer, which is unaffected by dust extinction. Methods. We cross-match the 60 μm selected Revised IRAS Faint Source Survey Redshift (RIFSCz) catalogue and the 150 MHz selected LOFAR value-added source catalogue in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) Spring Field. We estimate L150 for the cross-matched sources and compare it with the total infrared (IR) luminosity, LIR, and various SFR tracers. Results. We find a tight linear correlation between log L150 and log LIR for star-forming galaxies, with a slope of 1.37. The median qIR value (defined as the logarithm of the LIR to L150 ratio) and its rms scatter of our main sample are 2.14 and 0.34, respectively. We also find that log L150 correlates tightly with the logarithm of SFR derived from three different tracers, i.e., SFRHα based on the Hα line luminosity, SFR60 based on the rest-frame 60 μm luminosity and SFRIR based on LIR, with a scatter of 0.3 dex. Our best-fit relations between L150 and these SFR tracers are, log L150 (L⊙) = 1.35(±0.06) × log SFRHα (M⊙ yr−1) + 3.20(±0.06), log L150 (L⊙) = 1.31(±0.05) × log SFR60 (M⊙ yr−1) + 3.14(±0.06), and log L150 (L⊙) = 1.37 (±0.05) × log SFRIR (M⊙ yr−1) + 3.09(±0.05), which show excellent agreement with each other.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".