{"id":"W4365147116","doi":"10.1515/iupac.94.0625","title":"Lewis Adduct","year":2023,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Various Chemistry Research Topics","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Terminology; Meaning (existential); Abandonment (legal); Field (mathematics); Epistemology; Chemistry; Computer science; Linguistics; Philosophy; Mathematics; Political science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004827165,0.0006640297,0.0008103743,0.0001544929,0.0001774995,0.0001686061,0.001379617,0.0009549134,0.01168688],"category_scores_gemma":[0.002327559,0.0006654191,0.0003226144,0.0003852946,0.0002188547,0.00006630633,0.0007175338,0.002080614,0.000104152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009689973,"about_ca_system_score_gemma":0.001797793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00072283,"about_ca_topic_score_gemma":0.001196911,"domain_scores_codex":[0.9950855,0.00003291616,0.0006171993,0.0009304435,0.002361242,0.000972699],"domain_scores_gemma":[0.9965692,0.0001862584,0.0002584825,0.002012424,0.0005647629,0.0004089318],"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":[0.00006536173,0.0001811105,0.000006917019,0.001436305,0.0002502249,0.0006150557,0.00000766518,0.000003670008,0.0004887195,0.000001517466,0.9955868,0.00135668],"study_design_scores_gemma":[0.0006681766,0.00003480886,0.000002260376,0.0003992986,0.0001113957,0.00003959786,0.00004604277,0.00001689374,0.001546656,0.0001163564,0.9963861,0.0006324663],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001731418,0.0004903739,0.000003107082,0.0005875829,0.0003883206,0.0001078429,0.9970255,0.0003989617,0.0008251151],"genre_scores_gemma":[0.000004440897,0.001260875,0.00003434047,0.00007390531,0.002572448,0.00003833536,0.9768639,0.0001350071,0.0190168],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0201617,"threshold_uncertainty_score":0.9995797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02837242824598964,"score_gpt":0.4357260836822778,"score_spread":0.4073536554362882,"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."}}