{"id":"W6950093679","doi":"10.5281/zenodo.3832006","title":"Chapter 2.2 Status and Trends –Nature","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Environmental Conservation and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Biodiversity; Government (linguistics); Public policy","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":[0.001952017,0.0006299132,0.000349394,0.002096839,0.001124129,0.005386373,0.001082444,0.002202155,0.0889449],"category_scores_gemma":[0.003721305,0.000312623,0.0004545743,0.003511508,0.0009478823,0.004728887,0.001688664,0.003295098,0.04039921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002763304,"about_ca_system_score_gemma":0.004726574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007630549,"about_ca_topic_score_gemma":0.006138674,"domain_scores_codex":[0.9984389,0.0001323366,0.00006825423,0.0002382191,0.0009812196,0.0001411294],"domain_scores_gemma":[0.9988437,0.0002122379,0.0001218679,0.0001297659,0.0005674605,0.000125046],"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":[0.00002813804,0.00002185879,0.0003255861,0.0007313629,0.000005082051,0.00003455659,0.0003296781,0.0001913374,0.0006404666,0.07835866,0.8058977,0.1134355],"study_design_scores_gemma":[3.937708e-7,0.00000329711,0.0002695212,0.0001324889,6.043491e-7,0.00001402956,0.00003913774,0.000008215993,0.00004698446,0.001521187,0.9979621,0.000002053322],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001814701,0.07622782,0.002875583,0.04208803,0.02721275,0.0002119175,0.01002365,0.0009271734,0.8386183],"genre_scores_gemma":[0.02227997,0.07875533,0.005576126,0.014271,0.008784421,0.0005621111,0.0230351,0.001112481,0.8456234],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.0889449,"threshold_uncertainty_score":0.2975504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0129794197360068,"score_gpt":0.208772401469887,"score_spread":0.1957929817338802,"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."}}