{"id":"W4404981578","doi":"10.2139/ssrn.5023110","title":"From Fine to Feathers: Enforcement Stringency, Protectionism, and Biodiversity","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Protectionism; Feather; Biodiversity; Enforcement; International trade; Business; Biology; Ecology","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":[0.001737439,0.0002114871,0.0004463311,0.0009146033,0.0009700209,0.004416022,0.0005106053,0.001907905,0.03360293],"category_scores_gemma":[0.0168313,0.0002095316,0.0002544791,0.001136677,0.00428137,0.003910241,0.001753713,0.002399901,0.0006468037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009470034,"about_ca_system_score_gemma":0.0005841004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002901103,"about_ca_topic_score_gemma":0.003081075,"domain_scores_codex":[0.9990859,0.0002881536,0.00005977614,0.0002171504,0.0002381578,0.0001107447],"domain_scores_gemma":[0.9873169,0.008069326,0.002518946,0.0008909806,0.000540596,0.0006632606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0005598117,0.0004001318,0.07137787,0.000291695,0.0001496578,0.0005038846,0.003045406,0.004970471,0.001573971,0.7906474,0.01461001,0.1118696],"study_design_scores_gemma":[0.00003480275,0.0001135998,0.06170988,0.0001523037,0.00007447685,0.000218154,0.002670642,0.004840698,0.0005780998,0.9183606,0.01120703,0.00003962715],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7619715,0.004674806,0.01711715,0.02862668,0.0004393425,0.00003268564,0.0004866592,0.00006383604,0.1865873],"genre_scores_gemma":[0.9931803,0.000507283,0.000641686,0.0003393058,0.0001172116,0.000006833585,0.00004764866,0.00001568834,0.00514414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03360293,"threshold_uncertainty_score":0.112413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009998213634781018,"score_gpt":0.207304837377154,"score_spread":0.197306623742373,"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."}}