{"id":"W3142784578","doi":"10.2139/ssrn.3489245","title":"Subsidy Determination, Benchmarks and Adverse Inferences: Assessing ‘Benefit' in US – Coated Paper (Indonesia)","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"World Trade Organization Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Subsidy; Adverse effect; Business; Economics; Public economics; Natural resource economics; Medicine; Pharmacology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01960491,0.000246632,0.000691057,0.001568794,0.00144693,0.00521139,0.001013466,0.001734872,0.006017167],"category_scores_gemma":[0.1041734,0.0002885179,0.0004372342,0.001925218,0.002372332,0.002925723,0.001971911,0.002647338,0.0006903237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002733032,"about_ca_system_score_gemma":0.003130833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008941723,"about_ca_topic_score_gemma":0.009622803,"domain_scores_codex":[0.9870585,0.005563246,0.001196523,0.001229986,0.004105203,0.000846611],"domain_scores_gemma":[0.8943989,0.07712067,0.01301232,0.004830232,0.008925889,0.00171206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004681728,0.001789904,0.564449,0.0006689403,0.000614267,0.002143105,0.005681288,0.01539414,0.00162463,0.1784821,0.02273512,0.2017359],"study_design_scores_gemma":[0.0002450482,0.001633577,0.701274,0.0006467018,0.0008772971,0.0009642439,0.007219315,0.04770016,0.00697724,0.1961224,0.03612162,0.0002184281],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.89136,0.001257951,0.005867033,0.00369801,0.0002195724,0.0001391656,0.0005520336,0.00006709128,0.09683914],"genre_scores_gemma":[0.9954427,0.0001337356,0.00113878,0.0002748869,0.00003625797,0.00002338362,0.000108024,0.00001036681,0.002831696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01960491,"threshold_uncertainty_score":0.1036819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006667334021132554,"score_gpt":0.2594459601955844,"score_spread":0.2527786261744518,"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."}}