{"id":"W4247927502","doi":"10.1515/iupac.76.0251","title":"Henderson–Hasselbach Equation","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Relation (database); Hazard; Computer science; Toxicology; Medicine; Chemistry; Pharmacology; Data mining; Biology; Linguistics; Philosophy","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":[0.002185457,0.001094388,0.001530997,0.003652365,0.0006461429,0.003332634,0.002827107,0.001735262,0.1284129],"category_scores_gemma":[0.02286052,0.0005863301,0.001844338,0.005155867,0.0003477245,0.003215316,0.001277831,0.002338177,0.1119797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001842818,"about_ca_system_score_gemma":0.002568339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02797297,"about_ca_topic_score_gemma":0.02600159,"domain_scores_codex":[0.9978566,0.0003707079,0.0003011444,0.000786536,0.0004998888,0.0001851855],"domain_scores_gemma":[0.9956362,0.002222885,0.0002863992,0.0007772478,0.0009492455,0.0001280283],"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":[0.00009043519,0.00004168802,0.006728539,0.000590039,0.0001153092,0.00006744593,0.00004467458,0.005217321,0.00005551687,0.01406748,0.9153082,0.05767331],"study_design_scores_gemma":[0.0001892134,0.00003773359,0.008158929,0.0006939702,0.0001092917,0.0003541399,0.0001289413,0.02293772,0.0002912345,0.04069949,0.9263304,0.00006889064],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002878579,0.003437744,0.01370543,0.001592911,0.0005974796,0.0002064939,0.9482107,0.002074982,0.02729567],"genre_scores_gemma":[0.02576858,0.003689133,0.01833302,0.0009148312,0.0004037664,0.0006965279,0.9085439,0.001108709,0.0405416],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1284129,"threshold_uncertainty_score":0.4295841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0268848434196951,"score_gpt":0.3993791766574596,"score_spread":0.3724943332377645,"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."}}