{"id":"W2605327093","doi":"","title":"Laval University at TREC Dynamic Domain 2016: Subtopic extraction focused on Named Entities.","year":2016,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Lakehead University","funders":"","keywords":"Computer science; Domain (mathematical analysis); Extraction (chemistry); Information extraction; Named-entity recognition; Artificial intelligence; Engineering; Systems engineering; Chemistry; Mathematics; Chromatography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003779379,0.002588722,0.001546933,0.006669017,0.002539987,0.003040442,0.002414925,0.002244329,0.0231035],"category_scores_gemma":[0.0109947,0.0007518034,0.001077789,0.003424352,0.0007530657,0.005453718,0.00345847,0.002579618,0.02278036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002304154,"about_ca_system_score_gemma":0.006504895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06180754,"about_ca_topic_score_gemma":0.08761539,"domain_scores_codex":[0.996235,0.001090731,0.0003029764,0.0008931638,0.001069431,0.000408721],"domain_scores_gemma":[0.9927284,0.001570029,0.0002056339,0.001297844,0.003460687,0.0007374499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006122097,0.0004582837,0.00154479,0.001064273,0.00009556577,0.0002579203,0.0003432219,0.001309108,0.03521355,0.001795414,0.7967584,0.1605473],"study_design_scores_gemma":[0.0007216169,0.0004221651,0.01685389,0.0004754843,0.0002281369,0.0009621403,0.001267993,0.03270113,0.08925302,0.004648088,0.852206,0.0002603838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.08717807,0.01152781,0.0915122,0.007364033,0.004534156,0.003071953,0.6601518,0.06850435,0.06615562],"genre_scores_gemma":[0.05825792,0.001310693,0.1092922,0.0007768828,0.0004645367,0.001026219,0.7811112,0.003429234,0.0443311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06180754,"threshold_uncertainty_score":0.1228955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01461633347857493,"score_gpt":0.2508300623265508,"score_spread":0.2362137288479758,"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."}}