{"id":"W1881053730","doi":"","title":"E-Discovery Revisited: A Broader Perspective for IR Researchers","year":2007,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thomson Reuters (Canada)","funders":"","keywords":"Scope (computer science); Computer science; Task (project management); Data science; Perspective (graphical); Field (mathematics); NIST; Domain (mathematical analysis); Order (exchange); Information retrieval; Track (disk drive); Artificial intelligence; Natural language processing; Engineering; Systems engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003129272,0.0000776934,0.0001077257,0.00009251384,0.0005701845,0.0001654361,0.0002891493,0.0001120362,0.0006074546],"category_scores_gemma":[0.002304341,0.00007140415,0.0001040485,0.0004310877,0.0006228687,0.000526121,0.00003720103,0.0001352627,0.0001772042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004346949,"about_ca_system_score_gemma":0.0002069581,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009815348,"about_ca_topic_score_gemma":0.0111881,"domain_scores_codex":[0.9984312,0.00008941933,0.000188674,0.0002718583,0.000431189,0.0005876541],"domain_scores_gemma":[0.9983565,0.0008592777,0.00004187234,0.0001658272,0.0004330035,0.0001435013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006382597,0.00003476288,0.0003763563,0.00000331968,0.00001320124,0.000002930797,0.01362861,0.000002106032,0.000294465,0.9677625,0.01258061,0.005237324],"study_design_scores_gemma":[0.0001079396,0.000121268,0.0004111307,0.00002608272,0.00001313843,5.896146e-7,0.3999438,0.00004500317,0.01209387,0.1298889,0.4570421,0.000306193],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01348367,0.0002229898,0.11038,0.006946799,0.0002515566,0.0008479749,0.000005943809,0.00014904,0.8677121],"genre_scores_gemma":[0.9400694,0.00004524108,0.003660844,0.0006551482,0.0007667019,0.0000183119,0.000001395977,0.00001575023,0.05476717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9265858,"threshold_uncertainty_score":0.9967784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1836919306588136,"score_gpt":0.4938231282960004,"score_spread":0.3101311976371868,"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."}}