{"id":"W6893183437","doi":"10.5281/zenodo.1480384","title":"A Mixed Black and Whitelist Approach for Wildlife Trade Regulation in China: Biodiversity Conservation is Made of shades of grey","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wildlife; Biodiversity; Wildlife trade; Threatened species; Wildlife conservation; Listing (finance); Endangered species; Global biodiversity","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":[],"consensus_categories":[],"category_scores_codex":[0.004760016,0.0005561255,0.0002740016,0.001842412,0.004240264,0.002411042,0.001543385,0.001297893,0.006240969],"category_scores_gemma":[0.002408739,0.0002641713,0.000523268,0.001264537,0.003182136,0.002368917,0.003644731,0.001356349,0.0004835487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002998404,"about_ca_system_score_gemma":0.008963453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02741744,"about_ca_topic_score_gemma":0.06702287,"domain_scores_codex":[0.9972475,0.0007312886,0.0001170472,0.0004320872,0.0008592313,0.0006126781],"domain_scores_gemma":[0.9986054,0.0001551453,0.0001856147,0.0001481071,0.0003660732,0.0005396957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002744588,0.0003146185,0.1438055,0.001062731,0.0001828115,0.001454494,0.02010064,0.002823384,0.01275255,0.1520595,0.05895048,0.6062188],"study_design_scores_gemma":[0.0001562873,0.000711093,0.3022333,0.001847286,0.0003602392,0.001254288,0.02014815,0.01805408,0.007200771,0.0798367,0.5678455,0.0003524612],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.5381799,0.01124997,0.09346948,0.09230786,0.002882569,0.001125946,0.0004724193,0.0008902942,0.2594215],"genre_scores_gemma":[0.912059,0.002137329,0.02398513,0.01484883,0.0003779049,0.000443541,0.0002268639,0.00008738688,0.04583401],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.02741744,"threshold_uncertainty_score":0.05451566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0297550164904648,"score_gpt":0.2162661246415265,"score_spread":0.1865111081510617,"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."}}