{"id":"W4365147423","doi":"10.1515/iupac.94.0354","title":"Comproportionation","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; Comproportionation; Meaning (existential); Abandonment (legal); Chemistry; Computer science; Epistemology; Linguistics; Philosophy; Political science; Physical chemistry","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":[0.002310975,0.003580833,0.001678897,0.00457355,0.00243034,0.005878282,0.003394755,0.002229312,0.2380766],"category_scores_gemma":[0.01556903,0.0006650108,0.002598533,0.005528177,0.00124344,0.004493706,0.004248995,0.003387687,0.3026589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00171412,"about_ca_system_score_gemma":0.003373175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009788437,"about_ca_topic_score_gemma":0.02168525,"domain_scores_codex":[0.9959752,0.0007541359,0.000547906,0.001433603,0.0007736356,0.0005154527],"domain_scores_gemma":[0.9937109,0.001552692,0.0002876409,0.002893927,0.001220121,0.0003348111],"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":[0.00007014567,0.00002250074,0.000382201,0.0003375866,0.00001536277,0.00002648991,0.00002343097,0.0001360257,0.0001108083,0.001019901,0.9932758,0.004579803],"study_design_scores_gemma":[0.0001514854,0.00002500128,0.001370401,0.0003215377,0.00002104761,0.0001203426,0.000112956,0.0004790228,0.0005777925,0.005801519,0.990978,0.00004106461],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004085817,0.0002714766,0.0005542516,0.0002867704,0.0003678754,0.00006617594,0.9891967,0.002113541,0.00673463],"genre_scores_gemma":[0.0008421692,0.000134472,0.00101179,0.0002246225,0.00005281037,0.0002075766,0.9950718,0.0004359884,0.002018584],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2380766,"threshold_uncertainty_score":0.7964455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02750827549163299,"score_gpt":0.437680526251386,"score_spread":0.410172250759753,"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."}}