{"id":"W2785173651","doi":"10.3847/1538-4357/aaaa72","title":"Candidate Water Vapor Lines to Locate the H<sub>2</sub>O Snowline through High-dispersion Spectroscopic Observations. III. Submillimeter H<sub>2</sub> <sup>16</sup>O and H<sub>2</sub> <sup>18</sup>O Lines","year":2018,"lang":"en","type":"article","venue":"The Astrophysical Journal","topic":"Astrophysics and Star Formation Studies","field":"Physics and Astronomy","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Yukawa Institute for Theoretical Physics, Kyoto University; Science and Technology Facilities Council; National Astronomical Observatory of Japan; Queen's University; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Institutes of Natural Sciences; Japan Society for the Promotion of Science; Queen's University Belfast; University of Leeds","keywords":"Millimeter; Submillimeter Array; Snow line; Line (geometry); Water vapor; Infrared; Cosmic dust; Planet","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009767478,0.000227424,0.0001027363,0.0004231839,0.0001812131,0.0003091531,0.0002024434,0.0002680595,0.0008863486],"category_scores_gemma":[0.0002001271,0.0001346418,0.0001230624,0.0002887006,0.00009443452,0.0003699622,0.0002011919,0.000210048,0.0001734002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001098567,"about_ca_system_score_gemma":0.00008263125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002060143,"about_ca_topic_score_gemma":0.009680327,"domain_scores_codex":[0.9999604,0.000003297517,0.000001602768,0.00001246794,0.00001070935,0.00001154463],"domain_scores_gemma":[0.9998652,0.00002124803,0.0000570946,0.000009620327,0.00002237277,0.00002441453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002404084,0.00007037268,0.7351241,0.0000687081,0.00007137021,0.0002443261,0.0002083823,0.001393672,0.2336406,0.0002283402,0.0005093915,0.0282003],"study_design_scores_gemma":[0.00002700638,0.00007310654,0.9543053,0.00001847781,0.00004875145,0.0001347427,0.0003855765,0.01188936,0.03137583,0.0002076652,0.001523182,0.00001101616],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970654,0.0001780676,0.001438212,0.00002469861,0.000004013872,0.000006543963,0.0001267325,0.00004508987,0.001111335],"genre_scores_gemma":[0.9980622,0.00005392398,0.001497861,0.00001409284,0.000004781951,0.000003309368,0.0001232373,0.000006453059,0.0002341835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002060143,"threshold_uncertainty_score":0.00409627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01801549498515047,"score_gpt":0.2383058685864453,"score_spread":0.2202903736012949,"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."}}