{"id":"W4247046064","doi":"10.1515/iupac.83.0359","title":"Emission Anisotropy","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Glossary; Context (archaeology); Field (mathematics); Multidisciplinary approach; Computer science; Process (computing); Data science; Management science; Engineering; Sociology; Linguistics; Biology; Mathematics; Social science","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":[0.001989481,0.00210328,0.002070652,0.003241955,0.001459955,0.003413437,0.003746148,0.001921182,0.05263492],"category_scores_gemma":[0.006360565,0.0006610284,0.002012457,0.006650169,0.0006029017,0.002311837,0.002044697,0.003090088,0.1106537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002221465,"about_ca_system_score_gemma":0.001945954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01599514,"about_ca_topic_score_gemma":0.02886317,"domain_scores_codex":[0.9978783,0.0002690479,0.0002168466,0.0007964178,0.0005645154,0.0002748359],"domain_scores_gemma":[0.9968099,0.0006447381,0.0003350197,0.001079727,0.0009650563,0.0001656163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002040511,0.00006896541,0.002065704,0.0008301983,0.00005388152,0.00003503413,0.00002852604,0.0005668798,0.0005906955,0.001266046,0.983348,0.01094215],"study_design_scores_gemma":[0.0002324201,0.00004149602,0.009507488,0.0003463532,0.00005944597,0.0002258374,0.0001224972,0.001103966,0.002810348,0.00471144,0.9807587,0.00007988992],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000979438,0.0003820679,0.0008965107,0.0001772781,0.00008574376,0.00004130014,0.9905653,0.001940085,0.004932343],"genre_scores_gemma":[0.001775915,0.0002638633,0.001599808,0.0001284264,0.00002079053,0.0001853262,0.9932032,0.0002406452,0.002582104],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05263492,"threshold_uncertainty_score":0.1760814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01846105896136518,"score_gpt":0.4292501855144971,"score_spread":0.4107891265531319,"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."}}