{"id":"W6968664339","doi":"10.5281/zenodo.3965653","title":"A SEMANTIC METADATA ENRICHMENT SOFTWARE ECOSYSTEM BASED ON TOPIC METADATA ENRICHMENTS","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Metadata; Semantic grid; Metadata repository; Metadata modeling; Meta Data Services; Semantic computing; Geospatial metadata; Data element; Database catalog","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":[0.002630757,0.0005372298,0.0006941675,0.004796573,0.000932604,0.002221611,0.001082837,0.0007441101,0.002122009],"category_scores_gemma":[0.005707948,0.0005403908,0.001138377,0.002810023,0.0006905905,0.004148753,0.002895884,0.0009441876,0.001339928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000686125,"about_ca_system_score_gemma":0.001299523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002239117,"about_ca_topic_score_gemma":0.002962217,"domain_scores_codex":[0.9985868,0.0002631949,0.0002321389,0.0003041264,0.0005494809,0.00006427131],"domain_scores_gemma":[0.9964037,0.001274272,0.0002475354,0.0008649818,0.001026648,0.0001829017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000996861,0.0007799056,0.02526229,0.0008308566,0.0003264913,0.001007104,0.001865995,0.01576213,0.08368396,0.03339342,0.01313757,0.8229535],"study_design_scores_gemma":[0.0002075355,0.0003944282,0.009418798,0.0002070184,0.0002947371,0.00173635,0.0007108162,0.7018196,0.1444007,0.03321642,0.1073685,0.0002251132],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05254884,0.0002277766,0.9039702,0.0003426765,0.00005537426,0.0004337176,0.0006979635,0.03778113,0.003942281],"genre_scores_gemma":[0.1413021,0.0001774465,0.850854,0.0001358495,0.0000315454,0.0001994818,0.002719893,0.0008835536,0.003696081],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004796573,"threshold_uncertainty_score":0.01391292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04275775834215196,"score_gpt":0.2600206395142916,"score_spread":0.2172628811721397,"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."}}