{"id":"W3031304380","doi":"10.1093/mnras/staa1613","title":"The Herschel SPIRE Fourier Transform Spectrometer Spectral Feature Finder – III. Line identification and off-axis spectra","year":2020,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Entomological Society of America; Communal Studies Association; Chesapeake Research Consortium","keywords":"Spire (mollusc); Physics; Spectrometer; Spectral line; Line (geometry); Fourier transform; Noise (video); Detector; Identification (biology); Astrophysics; Remote sensing; Optics; Astronomy; Computer science; Artificial intelligence; Geometry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002391557,0.00115566,0.0008584338,0.004425768,0.0006789329,0.0009350067,0.001486947,0.0008214368,0.0221089],"category_scores_gemma":[0.002356522,0.0007587044,0.001388297,0.003144966,0.0002514904,0.001144945,0.001258526,0.0006032558,0.03493457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006416928,"about_ca_system_score_gemma":0.0008010677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006359724,"about_ca_topic_score_gemma":0.006039531,"domain_scores_codex":[0.9988501,0.0001208459,0.00006501227,0.000274477,0.0005206493,0.0001688664],"domain_scores_gemma":[0.9977189,0.0001705015,0.0004571702,0.00091171,0.0005151933,0.0002264447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001198444,0.0002595409,0.07156513,0.0006624032,0.0005184826,0.0004561009,0.0004649845,0.008657968,0.1131967,0.004286916,0.44023,0.3585034],"study_design_scores_gemma":[0.0006601241,0.0002273603,0.3398614,0.0001648535,0.0001481768,0.000792854,0.0001725427,0.04914489,0.07578158,0.008389357,0.5243816,0.0002753125],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1027146,0.0009914938,0.1498933,0.0003355252,0.0001102038,0.0006052121,0.5525486,0.1568109,0.0359902],"genre_scores_gemma":[0.09525377,0.0002116144,0.2546366,0.000291062,0.000118271,0.0005781987,0.6188859,0.01606591,0.01395863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0221089,"threshold_uncertainty_score":0.07396156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008134800530794496,"score_gpt":0.1901652613213008,"score_spread":0.1820304607905064,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}