{"id":"W3081627259","doi":"","title":"The Gap between Deep Learning and Law: Predicting Employment Notice.","year":2020,"lang":"en","type":"article","venue":"QSpace (Queen's University Library)","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Notice; Computer science; Law; Artificial intelligence; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002113374,0.0004781933,0.0002952312,0.00193499,0.0004369294,0.001644499,0.000895776,0.000829038,0.003853642],"category_scores_gemma":[0.01348624,0.0001839266,0.0004122405,0.001520342,0.0005230146,0.00316501,0.0009240223,0.002122316,0.001446481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002704427,"about_ca_system_score_gemma":0.001778508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02641256,"about_ca_topic_score_gemma":0.05927766,"domain_scores_codex":[0.99927,0.0002094385,0.00005072105,0.0001588357,0.0002168185,0.00009414004],"domain_scores_gemma":[0.9944824,0.003195208,0.0007777929,0.0004625292,0.0007942746,0.0002877676],"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":[0.0005788756,0.0008004594,0.2018639,0.0003169325,0.0001125693,0.0005753818,0.0005093406,0.1659135,0.002673218,0.02064618,0.05594039,0.5500693],"study_design_scores_gemma":[0.00002344198,0.00009971517,0.04590853,0.0001097675,0.00001793585,0.00009455728,0.0004290404,0.9088163,0.002605431,0.02574604,0.01611907,0.00003007976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8294963,0.004469687,0.1018059,0.01290616,0.0004996461,0.000186508,0.01372349,0.002098221,0.03481412],"genre_scores_gemma":[0.9782022,0.0003935258,0.01059675,0.0002840803,0.00007903693,0.00003171396,0.006305471,0.00004294046,0.004064224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02641256,"threshold_uncertainty_score":0.05251765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02700278706847899,"score_gpt":0.2505387508274037,"score_spread":0.2235359637589247,"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."}}