{"id":"W2478501622","doi":"","title":"Enforcement and Liability Challenges for Environmental Regulation of Deep Seabed Mining","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Mining and Resource Management","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Balsillie School of International Affairs; University of Waterloo","funders":"","keywords":"Enforcement; Jurisdiction; Liability; Anticipation (artificial intelligence); Business; Seabed; Environmental planning; Environmental law; Environmental resource management; Political science; Law; Fishery; Environmental science; Computer science; Finance","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":[0.0006589614,0.00008172202,0.0001016349,0.00004838842,0.00004698472,0.000005536238,0.0000574746,0.0000345329,0.00002332908],"category_scores_gemma":[0.00001183146,0.00005791457,0.00004053137,0.00001418822,0.00002742312,0.00004540186,0.00001635168,0.0001038897,9.745551e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002795494,"about_ca_system_score_gemma":0.00001752748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.607768e-7,"about_ca_topic_score_gemma":0.000008014577,"domain_scores_codex":[0.9990615,0.00001620989,0.0001726741,0.00009070746,0.0001027426,0.0005562303],"domain_scores_gemma":[0.9997817,0.00004593673,0.00004866352,0.00008395185,0.000005981059,0.00003379117],"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":[0.00004561885,0.0000285491,0.0008153643,0.00007753706,0.0002305656,1.655599e-7,0.0009571491,0.001868845,0.01343127,0.02457026,0.00001693276,0.9579577],"study_design_scores_gemma":[0.03119958,0.01014779,0.1337867,0.001308905,0.001211323,0.0005946285,0.08829736,0.2630369,0.0514615,0.228951,0.1858555,0.004148718],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.932164,0.00648509,0.05733204,0.0002578165,0.00009487093,0.0001915611,0.00000244536,0.0000361312,0.003436067],"genre_scores_gemma":[0.9943052,0.00476633,0.0002657553,0.000002694978,0.00007278722,0.000007731485,9.165564e-7,0.00001387853,0.0005647059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.953809,"threshold_uncertainty_score":0.2361688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006722918309553711,"score_gpt":0.1859305339130843,"score_spread":0.1792076156035306,"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."}}