The Herschel SPIRE Fourier Transform Spectrometer Spectral Feature Finder – III. Line identification and off-axis spectra
Bibliographic record
Abstract
ABSTRACT The European Space Agency Herschel Spectral and Photometric Imaging Receiver (SPIRE) Fourier Transform Spectrometer (FTS) Spectral Feature Finder (FF) project is an automated spectral feature fitting routine developed within the SPIRE instrument team to extract all prominent spectral features from all publicly available SPIRE FTS observations. We present the extension of the FF to include the off-axis detectors of the FTS in sparsely sampled single-pointing observations, the results of which have been ingested into the catalogue. We also present the results from an automated routine for identifications of the atomic/molecular transitions that correspond to the spectral features extracted by the FF. We use a template of 307 atomic fine structure and molecular lines that are commonly found in SPIRE FTS spectra for the cross-match. The routine makes use of information provided by the line identification to search for low signal-to-noise ratio features that have been excluded or missed by the iterative FF. In total, the atomic/molecular transitions of 178 942 lines are identified (corresponding to 83 per cent of the entire FF catalogue), and an additional 33 840 spectral lines associated with missing features from SPIRE FTS observations are added to the FF catalogue.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.035 |
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".