{"id":"W2258469095","doi":"10.1111/ddi.12400","title":"Use of expert knowledge to elicit population trends for the koala (<i>Phascolarctos cinereus</i>)","year":2016,"lang":"en","type":"article","venue":"Diversity and Distributions","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of Environment and Conservation","funders":"","keywords":"Phascolarctos cinereus; Bioregion; Population; Abundance (ecology); Range (aeronautics); Ecology; Geography; Population size; Marsupial; Wildlife; Biology; Estimation; Demography; Biodiversity","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":[0.04178694,0.000722491,0.0004958665,0.004401072,0.001383948,0.002235191,0.001123946,0.001920166,0.004407332],"category_scores_gemma":[0.1230685,0.0004837204,0.0006297787,0.001272802,0.0008596727,0.001904228,0.003029495,0.00118415,0.0006790856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002243062,"about_ca_system_score_gemma":0.002652304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002878214,"about_ca_topic_score_gemma":0.005843448,"domain_scores_codex":[0.9662561,0.02463578,0.00361833,0.001611024,0.003303987,0.0005747675],"domain_scores_gemma":[0.7697146,0.1960649,0.01132925,0.004846687,0.01717677,0.000867865],"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":[0.002769984,0.001163189,0.1149587,0.009613232,0.0005531416,0.002397368,0.1727522,0.03219216,0.04223162,0.006939197,0.008546337,0.6058829],"study_design_scores_gemma":[0.0009495851,0.006273793,0.2897069,0.01122539,0.001198645,0.002535049,0.2303157,0.2268444,0.0730767,0.06153584,0.09514444,0.001193567],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7856014,0.0005039078,0.1754291,0.002657674,0.00008883776,0.004342692,0.001957731,0.0002421131,0.02917659],"genre_scores_gemma":[0.8709087,0.0002930375,0.1230512,0.0006093229,0.0000590114,0.002607749,0.0007851355,0.0000197268,0.001666273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04178694,"threshold_uncertainty_score":0.2209931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04416711155499416,"score_gpt":0.2550919167658682,"score_spread":0.210924805210874,"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."}}