{"id":"W814617309","doi":"10.71781/10474","title":"An anonymizable entity finder in judicial decisions","year":2008,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008393409,0.0001682694,0.0003089498,0.0001593961,0.0008201918,0.0004091668,0.001324698,0.0004857718,0.01903586],"category_scores_gemma":[0.000668416,0.0001895623,0.00006994931,0.0005105461,0.0002177884,0.0007737944,0.0000539085,0.0003416047,0.001906937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001765182,"about_ca_system_score_gemma":0.001383943,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03861183,"about_ca_topic_score_gemma":0.6512572,"domain_scores_codex":[0.9978778,0.000244075,0.0004316763,0.0005027656,0.0005384794,0.0004052125],"domain_scores_gemma":[0.9989758,0.0001922688,0.0001813373,0.0003276477,0.0001664793,0.0001564256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003678567,0.001290232,0.005901545,0.000005331607,0.00004248111,0.0002241384,0.2021526,0.0003543322,0.000587338,0.00549888,0.01112726,0.7724479],"study_design_scores_gemma":[0.0002781296,0.0001498459,0.007045052,0.0003813081,0.00005995736,0.000001699382,0.08597838,0.0001851024,0.006771796,0.01615466,0.8817493,0.001244772],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5559574,0.00004349801,0.00002169521,0.00006193762,0.001020325,0.0005645705,0.00001797437,0.00000270656,0.4423099],"genre_scores_gemma":[0.9339084,0.0003298516,0.002798244,0.00004869776,0.0005423047,0.00005058649,0.0004474756,0.0000347125,0.06183973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.870622,"threshold_uncertainty_score":0.9988702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1595575328853501,"score_gpt":0.4736280693710158,"score_spread":0.3140705364856657,"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."}}