{"id":"W4394127874","doi":"10.6084/m9.figshare.1481062.v7","title":"Road characteristics best predict the probability of vehicle collisions with a non-native, hyperabundant ungulate","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Plant and fungal interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ungulate; Geography; Statistics; Environmental science; Biology; Ecology; Mathematics; Habitat","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":[0.001111088,0.001277393,0.001007772,0.001714243,0.0005118691,0.001089194,0.001960746,0.0009885404,0.03183073],"category_scores_gemma":[0.005726396,0.0004167825,0.001585959,0.002006196,0.0002815459,0.000849826,0.001101554,0.001284722,0.0215365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207239,"about_ca_system_score_gemma":0.001458492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0325073,"about_ca_topic_score_gemma":0.07027762,"domain_scores_codex":[0.9995133,0.0001087563,0.00004687108,0.0001892229,0.00006743221,0.0000744335],"domain_scores_gemma":[0.9981198,0.0008228399,0.0002528027,0.0003461112,0.0003295188,0.0001288823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0005188833,0.00009322613,0.0454828,0.001734414,0.0004833481,0.00006310559,0.0001148453,0.00301932,0.0005600955,0.000816304,0.9377525,0.009361105],"study_design_scores_gemma":[0.001605871,0.0001633101,0.2158863,0.00083023,0.00064542,0.0003280159,0.0004958102,0.006947955,0.001746857,0.00345666,0.7677536,0.0001401306],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003130615,0.0001114106,0.0001703429,0.0001134028,0.00003339962,0.0000114812,0.9954482,0.0003392906,0.0006420059],"genre_scores_gemma":[0.00957355,0.0001142629,0.001419368,0.00007024276,0.00001587956,0.000115255,0.9868612,0.0001924801,0.001637859],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0325073,"threshold_uncertainty_score":0.1064844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03794426089313552,"score_gpt":0.2364110616426304,"score_spread":0.1984668007494949,"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."}}