{"id":"W2577984450","doi":"","title":"Laval University and Lakehead University Experiments at TREC 2015 Contextual Suggestion Track","year":2015,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Université Laval","funders":"","keywords":"Ranking (information retrieval); Track (disk drive); Computer science; Construct (python library); Rank (graph theory); Learning to rank; Information retrieval; Artificial intelligence; Search engine; Machine learning; Programming language; Mathematics","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":[0.008557481,0.00127707,0.001203641,0.001584757,0.002911586,0.001670114,0.001677746,0.002000575,0.01139431],"category_scores_gemma":[0.02156348,0.0005680759,0.0007857349,0.002863809,0.0007670625,0.003159761,0.001669844,0.001986069,0.006086268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002770424,"about_ca_system_score_gemma":0.002959712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1095824,"about_ca_topic_score_gemma":0.2029879,"domain_scores_codex":[0.9913119,0.003800559,0.0006270549,0.001401002,0.002126971,0.0007325214],"domain_scores_gemma":[0.9807164,0.007972623,0.0006756685,0.003455697,0.005103255,0.002076399],"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":[0.009902696,0.01595289,0.02413046,0.001888561,0.0007383427,0.0005264518,0.001783923,0.02973967,0.02924761,0.00227178,0.6141018,0.2697158],"study_design_scores_gemma":[0.008594944,0.01702608,0.2208317,0.000316256,0.0009380433,0.0007829484,0.003658891,0.3159275,0.08159886,0.004580432,0.3446944,0.001049865],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8479025,0.003399003,0.01079025,0.00349011,0.001437366,0.002522182,0.04982593,0.02241416,0.05821853],"genre_scores_gemma":[0.7525941,0.000728783,0.05021447,0.001206139,0.00060066,0.001453691,0.145674,0.001456744,0.04607138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1095824,"threshold_uncertainty_score":0.2178892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06872451868132005,"score_gpt":0.2774112403357747,"score_spread":0.2086867216544547,"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."}}