{"id":"W2114077318","doi":"10.1890/02-0670","title":"META-ANALYSIS OF ANIMAL MOVEMENT USING STATE-SPACE MODELS","year":2003,"lang":"en","type":"article","venue":"Ecology","topic":"Insect Pheromone Research and Control","field":"Agricultural and Biological Sciences","cited_by":292,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Inference; State space; Bayesian probability; Machine learning; Process (computing); Statistical model; Trajectory; Data mining; Artificial intelligence; Bayesian inference; Data science; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.05483548,0.002135384,0.006061031,0.00704741,0.001041409,0.00482256,0.003675506,0.003060763,0.003094157],"category_scores_gemma":[0.1035518,0.001241577,0.0149949,0.007701836,0.00139575,0.00361307,0.001926406,0.003278595,0.0003141224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001911076,"about_ca_system_score_gemma":0.002158438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005603185,"about_ca_topic_score_gemma":0.005098437,"domain_scores_codex":[0.9517491,0.03941474,0.002071561,0.004896801,0.001500281,0.0003675481],"domain_scores_gemma":[0.8293405,0.1546789,0.004456399,0.009789684,0.001274084,0.0004604477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.002201548,0.0002687965,0.08619712,0.01142873,0.3802716,0.001122635,0.0007760986,0.2910005,0.001270433,0.09884256,0.007101298,0.1195188],"study_design_scores_gemma":[0.0006949301,0.0007553914,0.02321431,0.001615049,0.1464784,0.0004632536,0.0003395512,0.4548101,0.001599374,0.3543637,0.01539918,0.0002667711],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05186908,0.06750505,0.8685706,0.004174328,0.0008903174,0.0005189977,0.003852206,0.001085626,0.001533778],"genre_scores_gemma":[0.7037795,0.02264259,0.2635706,0.001070838,0.0005801088,0.001967557,0.004115877,0.0002963041,0.001976574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05483548,"threshold_uncertainty_score":0.2900012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1216795788974789,"score_gpt":0.2849770129552301,"score_spread":0.1632974340577512,"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."}}