{"id":"W2401350461","doi":"","title":"Learning Task Experiments in the TREC 2010 Legal Track.","year":2010,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Open Text (Canada)","funders":"","keywords":"Computer science; Track (disk drive); Task (project management); Artificial intelligence; Natural language processing; Information retrieval; Machine learning; Engineering; Operating system","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":[0.01971986,0.003429713,0.002481839,0.002042395,0.002454887,0.002060785,0.00340474,0.005121068,0.005969381],"category_scores_gemma":[0.0584663,0.001031763,0.002152692,0.002415413,0.001684713,0.005013828,0.003125643,0.005289467,0.003997553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002834202,"about_ca_system_score_gemma":0.003727158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02316017,"about_ca_topic_score_gemma":0.02560101,"domain_scores_codex":[0.9815134,0.009815409,0.001610156,0.002125252,0.003754562,0.001181272],"domain_scores_gemma":[0.9260101,0.05647479,0.002337592,0.005985394,0.006753121,0.00243897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.03386173,0.0942223,0.03265254,0.008133636,0.003023642,0.001723427,0.004226071,0.1018726,0.03847426,0.004512377,0.2630509,0.4142465],"study_design_scores_gemma":[0.04359426,0.07237931,0.1033854,0.0009594259,0.002900959,0.002466542,0.004464731,0.5113902,0.1053008,0.01867967,0.1328143,0.001664274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8917642,0.003805629,0.02546348,0.00211049,0.001561907,0.01274549,0.02565258,0.006411568,0.03048481],"genre_scores_gemma":[0.7694638,0.001304643,0.09558403,0.003598262,0.001217559,0.01557549,0.08288879,0.0007888345,0.02957845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02316017,"threshold_uncertainty_score":0.1042898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0219067442810115,"score_gpt":0.2715438169130098,"score_spread":0.2496370726319983,"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."}}