{"id":"W7118529629","doi":"10.5281/zenodo.18177994","title":"15/4. Kunming-Montreal Global Biodiversity Framework","year":2022,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biodiversity; Alien; Ecosystem; Alien species; Biodiversity conservation","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":[0.00293126,0.001607069,0.0009158551,0.004156159,0.001042896,0.00462675,0.00366001,0.001847213,0.1830369],"category_scores_gemma":[0.005259323,0.001094245,0.001323356,0.008806351,0.0005828586,0.002127274,0.00316896,0.002466398,0.05199543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01109545,"about_ca_system_score_gemma":0.02211353,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.710923,"about_ca_topic_score_gemma":0.6453957,"domain_scores_codex":[0.9984189,0.0002310928,0.00008320266,0.0001287811,0.0007493799,0.0003885998],"domain_scores_gemma":[0.9978359,0.0001590364,0.0001344352,0.0002137095,0.001274778,0.0003821128],"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":[0.00006348504,0.00002148811,0.0005277423,0.0005422324,0.00003604331,0.00003595988,0.00009953586,0.0007503165,0.0001728761,0.02661563,0.9489397,0.02219505],"study_design_scores_gemma":[0.00004941444,0.00001102064,0.003754436,0.0002896023,0.000019691,0.00001882971,0.00006610851,0.0004279432,0.000200472,0.003064026,0.9920696,0.00002895994],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0006361317,0.001854558,0.004260115,0.002381722,0.0006387866,0.0004855723,0.7212565,0.004427546,0.2640591],"genre_scores_gemma":[0.02301663,0.004046764,0.03122518,0.002164983,0.0002153195,0.002621655,0.6823391,0.006986195,0.2473842],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.710923,"threshold_uncertainty_score":0.6123195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02307580044400813,"score_gpt":0.2274113826296491,"score_spread":0.204335582185641,"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."}}