{"id":"W2155459813","doi":"10.1186/1758-2946-3-20","title":"Chemical Entity Semantic Specification: Knowledge representation for efficient semantic cheminformatics and facile data integration","year":2011,"lang":"en","type":"article","venue":"Journal of Cheminformatics","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Cheminformatics; Representation (politics); Semantic Web; Semantics (computer science); Knowledge representation and reasoning; Chemical database; Domain (mathematical analysis); Data science; Information retrieval; Artificial intelligence; Chemistry; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002152344,0.0001898011,0.0003742425,0.0001278505,0.0001270827,0.0002145674,0.0009026948,0.00009828655,0.0001879064],"category_scores_gemma":[0.001484284,0.0001531385,0.00006375415,0.0001939291,0.0001883864,0.001229687,0.0003005939,0.0001811922,0.000051106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006951664,"about_ca_system_score_gemma":0.0001110384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004850567,"about_ca_topic_score_gemma":0.000001057997,"domain_scores_codex":[0.9977262,0.00001829894,0.001352443,0.0001698947,0.0004672389,0.000265946],"domain_scores_gemma":[0.9972175,0.0002183334,0.001347509,0.0005972754,0.000477873,0.0001414658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002360177,0.0003910358,0.0003973458,0.001692926,0.00004099807,0.000003652591,0.03751549,0.0002749069,0.9374316,0.001196539,0.009706947,0.01111255],"study_design_scores_gemma":[0.0006445894,0.00007780271,0.0003221415,0.0001857474,0.00009137793,0.0002034595,0.001499304,0.1568438,0.8385962,0.0003673696,0.0009419913,0.0002261508],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8880899,0.00008016077,0.1060483,0.00008714356,0.0007837354,0.0003975903,0.00004796436,0.00004116911,0.00442406],"genre_scores_gemma":[0.8973515,0.00003889011,0.1022023,0.00003223688,0.0002020181,0.0000069626,0.00005239133,0.0000131622,0.0001005725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1565689,"threshold_uncertainty_score":0.6244806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08371741914909708,"score_gpt":0.3216287515449943,"score_spread":0.2379113323958972,"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."}}