{"id":"W2805261386","doi":"","title":"DRAFT – Cold Start Knowledge Base Population with the Knowledge Resolver System for TAC-KBP 2015 – DRAFT.","year":2015,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Resolver; Knowledge base; Population; Computer science; Medicine; Telecommunications; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005006694,0.0008449072,0.0007436954,0.001208565,0.001239954,0.00347478,0.002894454,0.002040778,0.07040419],"category_scores_gemma":[0.03124752,0.001157918,0.0009625203,0.001148031,0.0006566087,0.004070969,0.002766748,0.002607872,0.04812584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009589868,"about_ca_system_score_gemma":0.004749329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01103987,"about_ca_topic_score_gemma":0.01411806,"domain_scores_codex":[0.9965682,0.001012237,0.0002663177,0.0006165886,0.001257249,0.0002793721],"domain_scores_gemma":[0.9833488,0.003333915,0.0002667101,0.004893721,0.007553781,0.0006029747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001217318,0.0004625766,0.001221686,0.0006359525,0.000249402,0.0003656843,0.0005366197,0.01598958,0.01067595,0.02925107,0.7227495,0.2166446],"study_design_scores_gemma":[0.0009707119,0.0004341744,0.002936703,0.0003677099,0.0001709701,0.0006997432,0.0005357505,0.215562,0.04779701,0.09497733,0.6353334,0.0002145266],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0176439,0.0005798937,0.7679477,0.004726281,0.002605008,0.002573015,0.04834939,0.09399825,0.06157651],"genre_scores_gemma":[0.09571031,0.0002328589,0.7222672,0.0009119593,0.0003402818,0.001352106,0.1207826,0.0103789,0.04802389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07040419,"threshold_uncertainty_score":0.2355255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710269696056475,"score_gpt":0.2730996170518621,"score_spread":0.2559969200912973,"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."}}