{"id":"W3129863057","doi":"","title":"AI and Legal Analytics","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Analytics; Legal research; Legal profession; Big data; Statute; Service provider; Data science; Empirical legal studies; Legal case; Business; Law; Internet privacy; Computer science; Political science; Service (business); Data mining","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":[0.001174082,0.00007455417,0.0001074324,0.00003127622,0.0005745901,0.0001684566,0.0002288704,0.00006250813,0.0001152043],"category_scores_gemma":[0.0003063577,0.00007107418,0.00005351895,0.0002353933,0.0002426114,0.0003315175,0.00002769767,0.001190942,0.00007014757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003035746,"about_ca_system_score_gemma":0.001924328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006002372,"about_ca_topic_score_gemma":0.007319689,"domain_scores_codex":[0.9979049,0.000106975,0.0001797445,0.0001323789,0.0002931518,0.001382872],"domain_scores_gemma":[0.9995475,0.00004282566,0.00006626228,0.00005038204,0.00008473238,0.0002082336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001397956,0.000009009102,0.0008642889,8.777099e-7,0.00002549779,0.00000654515,0.002195089,0.0000259249,0.00006362288,0.9728256,0.000345179,0.0236244],"study_design_scores_gemma":[0.0001468229,0.0003821202,0.00006478683,0.000009052433,0.00005392734,0.00008549492,0.03693626,0.0014435,0.0002443816,0.5496157,0.4107195,0.0002985139],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4042541,0.00561555,0.1369651,0.3853722,0.000976975,0.0004458187,0.00000413763,0.0002743686,0.06609169],"genre_scores_gemma":[0.9930913,0.002367073,0.00005423351,0.002333221,0.000948925,5.743396e-7,2.263548e-7,0.000009151036,0.001195239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5888372,"threshold_uncertainty_score":0.5174118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03220723471869312,"score_gpt":0.334804672814761,"score_spread":0.3025974380960678,"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."}}