{"id":"W2126105412","doi":"","title":"Overview of the TREC 2008 Legal Track","year":2008,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Open Text (Canada)","funders":"","keywords":"Relevance (law); Relevance feedback; Computer science; Track (disk drive); Information retrieval; World Wide Web; Artificial intelligence; Image retrieval; Political science; Law","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":[0.0221266,0.001110689,0.001328122,0.01614382,0.003957005,0.01104287,0.003177055,0.002800257,0.02977487],"category_scores_gemma":[0.0252609,0.001394162,0.0009415734,0.01734117,0.001094175,0.008032067,0.002541755,0.002924547,0.02477612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01171982,"about_ca_system_score_gemma":0.01821452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1313708,"about_ca_topic_score_gemma":0.1444392,"domain_scores_codex":[0.983435,0.00367893,0.001190083,0.001533658,0.008791107,0.00137136],"domain_scores_gemma":[0.9709673,0.003745179,0.001794717,0.002561393,0.01847508,0.002456427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002626532,0.0003906679,0.001743075,0.0008018607,0.00003913616,0.00005530823,0.0002782775,0.001435744,0.002433779,0.008542554,0.7976138,0.1864031],"study_design_scores_gemma":[0.00009897305,0.0002633604,0.007571129,0.0003811723,0.00003859479,0.00009490355,0.0002130422,0.002844184,0.002391494,0.002507268,0.9834889,0.0001071455],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"review","genre_scores_codex":[0.03124036,0.05002619,0.07407966,0.04989394,0.006295204,0.01805985,0.2354418,0.03876975,0.4961933],"genre_scores_gemma":[0.09571303,0.02593195,0.2131398,0.009777281,0.004290876,0.01291599,0.4354052,0.005639345,0.1971865],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.1313708,"threshold_uncertainty_score":0.2612122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1983185289873794,"score_gpt":0.3952097771901537,"score_spread":0.1968912482027742,"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."}}