{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002963621,0.000257487,0.0002990914,0.0002794638,0.0002281369,0.00008320731,0.001076636,0.0001563959,0.0003233477],"category_scores_gemma":[0.0001020722,0.0002060219,0.0001453247,0.0005336136,0.0001934676,0.0008866311,0.0003292324,0.0001950574,0.0003997454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007857753,"about_ca_system_score_gemma":0.0001820815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005223266,"about_ca_topic_score_gemma":0.0001864591,"domain_scores_codex":[0.997932,0.0001835838,0.0002566145,0.0007109216,0.0004985273,0.0004184163],"domain_scores_gemma":[0.9982097,0.000286981,0.0002352111,0.0009259339,0.0001893536,0.0001527918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009363952,0.0003102421,0.001987146,0.00003440549,0.0001427191,0.0002550378,0.0008759425,0.000005183613,0.3311905,0.3202887,0.002429631,0.3415441],"study_design_scores_gemma":[0.009006059,0.002714645,0.07055749,0.0009908323,0.0001990191,0.0001163953,0.0003004104,0.01270079,0.5190481,0.1809664,0.1991639,0.004235967],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2430014,0.00004194897,0.7491952,0.001091679,0.0001997748,0.0002167305,0.00001198886,0.0006456585,0.005595607],"genre_scores_gemma":[0.9671293,0.0002505233,0.01017635,0.00005959228,0.00002678757,0.000001898794,0.000004463973,0.00001374306,0.02233732],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7390189,"threshold_uncertainty_score":0.8401328,"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."}}