{"id":"W4252025728","doi":"10.1515/iupac.79.2222","title":"Absorption","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":"Terminology; Glossary; Chemical nomenclature; Relation (database); Meaning (existential); Computer science; Epistemology; Chemistry; Management science; Linguistics; Engineering; Philosophy; Data mining; Organic 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":[0.001517523,0.002451953,0.001669896,0.00358503,0.001254757,0.003986289,0.003906818,0.002235179,0.1441119],"category_scores_gemma":[0.009498095,0.00063002,0.002189321,0.005268268,0.0005499285,0.002867369,0.003013531,0.00240042,0.2740198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00203112,"about_ca_system_score_gemma":0.002760974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01696197,"about_ca_topic_score_gemma":0.03396631,"domain_scores_codex":[0.9972882,0.0004075239,0.0003158874,0.0009469462,0.0006575796,0.0003838467],"domain_scores_gemma":[0.9962748,0.0006760397,0.0003039708,0.001126262,0.001330259,0.0002887218],"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":[0.00011304,0.00003001168,0.001553,0.0004679589,0.00002613931,0.00002309423,0.00002374233,0.0001931099,0.00009180081,0.000807811,0.9897292,0.006941051],"study_design_scores_gemma":[0.0001169036,0.00002197082,0.002918409,0.0003099631,0.00002279542,0.00009871131,0.000108799,0.0004436901,0.0003353367,0.00208319,0.9935156,0.00002457266],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003314375,0.0001929938,0.0002585426,0.0002192039,0.0001106707,0.00004227875,0.9940064,0.0009034352,0.00393492],"genre_scores_gemma":[0.0006562356,0.0001032174,0.0005561027,0.0001849126,0.0000184265,0.0000957588,0.9958557,0.0001111966,0.00241844],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1441119,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351679729580335,"score_gpt":0.3837381133943035,"score_spread":0.3702213160985002,"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."}}