{"id":"W2801101691","doi":"10.1002/ece3.4823","title":"Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses","year":2019,"lang":"en","type":"preprint","venue":"Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Covariate; Computer science; Selection (genetic algorithm); Inference; Model selection; Data mining; Statistical inference; Tracking (education); Movement (music); Statistical model; Exploratory data analysis; Process (computing); Statistics; Machine learning; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008947707,0.0001836986,0.000251704,0.00006936988,0.0004050107,0.00006019387,0.0001776963,0.0003515149,0.00009690939],"category_scores_gemma":[0.0001620114,0.0001973922,0.00003314283,0.00007354407,0.0001506906,0.0005501434,0.0008304609,0.0003075198,0.0000215312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002391673,"about_ca_system_score_gemma":0.00003397743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000157091,"about_ca_topic_score_gemma":0.002083679,"domain_scores_codex":[0.9985296,0.0001095993,0.0002785407,0.0007257658,0.00007933534,0.0002772129],"domain_scores_gemma":[0.9992278,0.0001966701,0.0002469519,0.0002688885,0.00001790741,0.00004181735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005101817,0.00003156329,0.9913208,0.00007269924,0.00006579339,9.121171e-7,0.0001077846,0.001334746,0.003321256,0.0002568892,0.002151021,0.0012855],"study_design_scores_gemma":[0.000325327,0.0001352764,0.9439652,0.00002234971,0.0001343696,0.000007664736,0.000195977,0.04858316,0.0001354713,0.006193574,0.0001068346,0.0001948115],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986476,0.0001836121,0.01017572,0.001575856,0.0004964647,0.0006744113,0.00004284449,0.00004318092,0.0003319341],"genre_scores_gemma":[0.9968842,0.00006317897,0.001901091,0.0004574916,0.0001186527,0.00007098667,0.0003247902,0.00001238841,0.0001672024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04735563,"threshold_uncertainty_score":0.8049423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1024914487772564,"score_gpt":0.3178048006535831,"score_spread":0.2153133518763267,"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."}}