{"id":"W4254995623","doi":"10.1515/iupac.79.1838","title":"Potentiation","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":"Long-term potentiation; Chemistry; Computer science; Biochemistry","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":[0.001337533,0.002086264,0.001427692,0.003129311,0.001015838,0.003469995,0.002824417,0.001838334,0.1601006],"category_scores_gemma":[0.01016584,0.0005618169,0.002022012,0.004766393,0.0003580519,0.002513217,0.002173176,0.001795378,0.2395534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001562098,"about_ca_system_score_gemma":0.002725154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01545457,"about_ca_topic_score_gemma":0.03013431,"domain_scores_codex":[0.9977971,0.0003378466,0.0003083342,0.0008415332,0.0004562733,0.000258952],"domain_scores_gemma":[0.9962801,0.0008193855,0.0003064751,0.001137028,0.001166137,0.0002908894],"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":[0.0001181816,0.00002208517,0.001334112,0.0006740598,0.00003706808,0.00001866246,0.00001582049,0.0001659093,0.00008599931,0.0006412172,0.9895121,0.007374729],"study_design_scores_gemma":[0.0001406916,0.00002141708,0.003042606,0.000378982,0.000034548,0.00006677828,0.00006356175,0.0002882315,0.0002386619,0.001550173,0.9941505,0.00002399316],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001449396,0.0001304222,0.0001237255,0.0001079739,0.00006293685,0.0000260261,0.9967535,0.0004450798,0.002205446],"genre_scores_gemma":[0.0003931525,0.00009357776,0.0003555817,0.0001456561,0.00001388307,0.00008654677,0.9972957,0.00007499004,0.001540929],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1601006,"threshold_uncertainty_score":0.5355899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087984495890895,"score_gpt":0.3778672697348273,"score_spread":0.3669874247759184,"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."}}