{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0001649962,0.0001277551,0.0001483735,0.00003252857,0.002477552,0.0002555407,0.0004420061,0.0001135081,0.0001870398],"category_scores_gemma":[0.0001982976,0.0001239261,0.00006263369,0.0003661653,0.0007224298,0.00118335,0.0003547381,0.0003712837,0.00007133761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005908461,"about_ca_system_score_gemma":0.0000888782,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01273326,"about_ca_topic_score_gemma":0.000831439,"domain_scores_codex":[0.9984378,0.0004036834,0.0001245697,0.0003028316,0.0003270423,0.0004040862],"domain_scores_gemma":[0.9987687,0.0006382607,0.000111113,0.0001124685,0.00003629985,0.0003331731],"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":[0.0001122585,0.00003133408,0.4497621,0.00002548806,0.0001269677,0.0000896694,0.05874501,0.0002772035,0.000009062896,0.4619829,0.02046113,0.008376841],"study_design_scores_gemma":[0.00006990934,0.00009135526,0.001431828,0.00001805033,0.00003891756,2.641509e-8,0.04057956,0.00008293151,0.0004352089,0.001027644,0.9560328,0.0001918043],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1885108,0.00008393121,0.002221193,0.6123402,0.0003288882,0.000656236,0.0000141487,0.001083348,0.1947613],"genre_scores_gemma":[0.9783705,0.0003136053,0.0006738603,0.0004330599,0.0003987645,4.5805e-7,0.00000435783,0.00002020942,0.01978512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9355716,"threshold_uncertainty_score":0.9988211,"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."}}