{"id":"W2522717465","doi":"10.1111/2041-210x.12660","title":"Predictive modelling of ecological patterns along linear‐feature networks","year":2016,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Alberta; University of Alberta","funders":"Mountain Equipment Co-operative; Alberta Conservation Association","keywords":"Feature (linguistics); Feature selection; Computer science; Metric (unit); Foothills; Kriging; Linear model; Set (abstract data type); Variable (mathematics); Geostatistics; Data mining; Ecology; Cartography; Geography; Machine learning; Spatial variability; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001094345,0.00008205334,0.0001613272,0.00003924973,0.00007584236,0.000001222774,0.00007626508,0.0003471932,0.0001933341],"category_scores_gemma":[0.0001700668,0.0000592534,0.0000254376,0.0001068944,0.0002443945,0.0001359924,0.00009565798,0.0001585845,0.000007116503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001253495,"about_ca_system_score_gemma":0.000009046108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005316879,"about_ca_topic_score_gemma":0.0005016062,"domain_scores_codex":[0.9987795,0.0005414989,0.0001939426,0.0002381685,0.00004331238,0.0002035785],"domain_scores_gemma":[0.9991484,0.000624494,0.00009272318,0.00009229111,0.000009012309,0.00003304124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005960833,0.00005157792,0.9590204,0.000002265132,0.000006532112,0.000001833873,0.00005026485,0.03656605,0.0001594654,0.0002282429,0.00006973907,0.003784024],"study_design_scores_gemma":[0.000272045,0.0001297051,0.8296841,0.000008871119,0.000009838795,0.000005240511,0.00002187466,0.1646378,0.00004327208,0.005093223,0.00003574861,0.00005822672],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5420461,0.00002133317,0.4572491,0.0003372754,0.0001295526,0.00007790278,0.000001467656,0.000009088425,0.00012821],"genre_scores_gemma":[0.9458629,0.00007154972,0.05374703,0.0001303244,0.00003375243,0.00002847653,0.0000018949,0.000003721933,0.0001203487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4038168,"threshold_uncertainty_score":0.2677872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02334905235338137,"score_gpt":0.284028102181711,"score_spread":0.2606790498283297,"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."}}