{"id":"W4387717079","doi":"10.1364/fts.2023.ftu5b.3","title":"Using solar-viewing FTS observations to inform spectroscopic parameters","year":2023,"lang":"en","type":"article","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Remote sensing; Atmospheric composition; Line (geometry); Computer science; Absorption (acoustics); Environmental science; Physics; Meteorology; Optics; Geology; Atmosphere (unit)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004662126,0.0003227053,0.0001949062,0.0008417821,0.0002308437,0.0009701077,0.0002935544,0.0003957994,0.001333007],"category_scores_gemma":[0.001782768,0.0001701139,0.0001469596,0.001132272,0.000205401,0.001083565,0.0003634019,0.0003505569,0.0007255867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005377008,"about_ca_system_score_gemma":0.0003756887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01011984,"about_ca_topic_score_gemma":0.01595252,"domain_scores_codex":[0.9997717,0.00004872134,0.00001859736,0.00006999641,0.00007759609,0.00001341825],"domain_scores_gemma":[0.9994996,0.00008951323,0.0001516938,0.0001302938,0.0001086993,0.00002030066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003403893,0.0001709423,0.5070038,0.0002232826,0.0002391806,0.0001563079,0.0003636799,0.05636265,0.2156252,0.005297492,0.008551219,0.2056659],"study_design_scores_gemma":[0.00008952199,0.00009181229,0.4370784,0.0001229591,0.0001693132,0.000218334,0.0004744673,0.3493259,0.1561247,0.01137635,0.04478266,0.0001456782],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8519272,0.00105828,0.104229,0.0007015173,0.0001982388,0.00005774749,0.01725939,0.002516741,0.0220519],"genre_scores_gemma":[0.9630937,0.0003802927,0.03163083,0.000115662,0.00004634505,0.00001275123,0.003815932,0.0001678148,0.0007364725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01011984,"threshold_uncertainty_score":0.02012193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06596549735884419,"score_gpt":0.2728470764613382,"score_spread":0.206881579102494,"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."}}