High-<i>z</i> Sudoku: a diagnostic tool for identifying robust (sub)mm redshifts
Bibliographic record
Abstract
ABSTRACT We present methods to (i) graphically identify robust redshifts using emission lines in the (sub)mm regime, (ii) evaluate the capabilities of different (sub)mm practices for measuring spectroscopic redshifts, and (iii) optimize future (sub)mm observations towards increasing the fraction of robust redshifts. Using this publicly available code (https://github.com/tjlcbakx/redshift-search-graphs), we discuss scenarios where robust redshifts can be identified using both single- and multiple-line detections, as well as scenarios where the redshift remains ambiguous, even after the detection of multiple lines. Using the redshift distribution of (sub)mm samples, we quantify the efficiencies of various practices for measuring spectroscopic redshifts, including interferometers, as well as existing and future instruments specifically designed for redshift searches. Finally, we provide a method to optimize the observation strategy for future (sub)mm spectroscopic redshift searches with the Atacama Large Millimetre/submillimetre Array, where 2 mm proves indispensable for robust redshifts in the $\mathit{ z}$ = 2−4 region.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".