{"id":"W4411890121","doi":"10.3390/catal15070636","title":"A Scoring Model for Catalyst Informatics Based on Real-Time High-Throughput Fluorogenic Assay for Catalyst Discovery and Kinetic Profiling","year":2025,"lang":"en","type":"article","venue":"Catalysts","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Profiling (computer programming); Catalysis; Throughput; Chemistry; Computer science; Biochemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002150398,0.0004238137,0.0006343757,0.0003025081,0.0005128005,0.000573855,0.0007279136,0.000155807,0.000008539814],"category_scores_gemma":[0.001164942,0.0003812721,0.0001429912,0.000373083,0.0002393038,0.0007817733,0.000276115,0.000129983,0.00004723547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002030695,"about_ca_system_score_gemma":0.0004567515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000211753,"about_ca_topic_score_gemma":0.00002194512,"domain_scores_codex":[0.9971511,0.00008913512,0.0008626443,0.0007506808,0.0004100764,0.0007363546],"domain_scores_gemma":[0.9974734,0.0008209585,0.0004076951,0.0009695909,0.0001861628,0.0001422289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002495167,0.00007726555,0.0001640735,0.001349329,0.00002405656,0.000001935158,0.0005109064,0.1239893,0.8700022,0.002451389,0.0007224681,0.0004575745],"study_design_scores_gemma":[0.0008601526,0.00009242892,0.00008515012,0.0002469632,0.0001061201,0.000003345006,0.00004863069,0.669688,0.3278967,0.0005765865,0.00006905982,0.0003268569],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7102904,0.00005638325,0.2858641,0.0001788957,0.0006540288,0.001764298,0.0005297464,0.0002384683,0.0004237237],"genre_scores_gemma":[0.8982608,0.000007562816,0.09869248,0.0002337025,0.0001131221,0.0006167527,0.0006409158,0.00005258323,0.001382057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5456986,"threshold_uncertainty_score":0.9998639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01188331142663106,"score_gpt":0.2696934102768905,"score_spread":0.2578100988502595,"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."}}