{"id":"W7034582089","doi":"","title":"USMCA Not A Win For America!","year":2019,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Compromise; Sovereignty; Copying; Silver bullet; Government (linguistics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006389076,0.0005535922,0.0003316025,0.0008872274,0.003434885,0.006520541,0.0004833254,0.003075333,0.4066262],"category_scores_gemma":[0.003568379,0.0003152406,0.0003611236,0.001932556,0.0006063539,0.005119885,0.002212277,0.003486031,0.2404122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002126962,"about_ca_system_score_gemma":0.004640328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03995342,"about_ca_topic_score_gemma":0.07782347,"domain_scores_codex":[0.999454,0.00005183402,0.00001434213,0.00007081142,0.0002939971,0.0001150249],"domain_scores_gemma":[0.9986556,0.0001037571,0.00005038233,0.0001434579,0.0006833989,0.0003634776],"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":[0.000003783584,0.00000420022,0.0000507097,0.000009379635,4.430823e-7,0.000008925784,0.00001260456,0.000005279478,0.00002159011,0.003051917,0.9884737,0.008357499],"study_design_scores_gemma":[0.000001010671,0.000001239491,0.0001003657,0.00001394137,4.299502e-7,0.000006641976,0.0000370863,0.00001127266,0.0000223999,0.0004439375,0.9993604,0.000001395038],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.0007786859,0.001725165,0.0003986758,0.04755917,0.007466269,0.00002719932,0.004169684,0.001105602,0.9367695],"genre_scores_gemma":[0.004092256,0.0005817961,0.0003119016,0.007424715,0.0003459384,0.00002160542,0.001653783,0.0003457148,0.9852224],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.4066262,"threshold_uncertainty_score":0.8463757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008282294880687808,"score_gpt":0.1895182454264135,"score_spread":0.1812359505457257,"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."}}