{"id":"W6904927570","doi":"10.14457/cu.the.2015.1075","title":"BUILDING A SYNERGISTIC MODEL ON CHEMICAL AND WASTE MULTILATERAL ENVIRONMENTAL AGREEMENTS TO IMPROVE ENVIRONMENTAL ENFORCEMENT : A CASE STUDY OF MULTILATERAL ENVIRONMENTAL AGREEMENTS REGIONAL ENFORCEMENT NETWORK","year":2015,"lang":"en","type":"dataset","venue":"NRCT Data Center","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Enforcement; Documentation; Key (lock); Law enforcement; Capacity building","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":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001179238,0.002755811,0.00206738,0.0006633521,0.0004649949,0.0002488004,0.002997626,0.0006701639,0.001302473],"category_scores_gemma":[0.00003562531,0.002544906,0.0003146204,0.0001700136,0.0005527131,0.000929678,0.01120617,0.001413559,0.0008419605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003555026,"about_ca_system_score_gemma":0.00009689359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001622255,"about_ca_topic_score_gemma":0.0002461939,"domain_scores_codex":[0.9853248,0.0004407016,0.002982337,0.004419447,0.004301249,0.002531454],"domain_scores_gemma":[0.9912345,0.0001073222,0.001632272,0.005392141,0.00001922584,0.001614517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004295411,0.01477579,0.002576427,0.0002261402,0.002691853,0.003126372,0.001582465,0.01086966,0.01746525,0.000001218201,0.9404375,0.00195194],"study_design_scores_gemma":[0.08539654,0.01397001,0.000505541,0.002385087,0.006999562,0.002670214,0.01070093,0.28749,0.00248257,0.00003209306,0.5747567,0.01261079],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.427578,0.0000521359,0.00003170169,0.000006469997,0.0004984254,0.003866039,0.5679253,0.00003351724,0.000008441892],"genre_scores_gemma":[0.4711092,0.0000472429,0.0007410466,0.0003019859,0.0004026961,0.0003365163,0.526673,0.0002315425,0.000156861],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3656808,"threshold_uncertainty_score":0.999936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04704197138843065,"score_gpt":0.2984400564823879,"score_spread":0.2513980850939572,"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."}}