{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003333591,0.00007438141,0.0004222885,0.00002479847,0.00006802488,0.00001075774,0.0001073417,0.00004343348,0.003197698],"category_scores_gemma":[0.00002234399,0.00002614791,0.0003688349,0.0004230015,0.00003674212,0.00005430223,0.00002550809,0.00005489683,0.000006168992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002132075,"about_ca_system_score_gemma":0.00001350973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005307677,"about_ca_topic_score_gemma":0.002626021,"domain_scores_codex":[0.9990402,0.0002185318,0.0001664123,0.0001665095,0.0001369039,0.000271471],"domain_scores_gemma":[0.9996049,0.0001441884,0.00007233258,0.00004568992,0.00007176651,0.00006105841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000948812,0.0002764797,0.002843013,0.000002884169,0.04797399,0.00001194201,0.0001060741,0.008430111,0.9288963,0.01087049,0.0001017046,0.0003921304],"study_design_scores_gemma":[0.001808951,0.009702174,0.1625322,0.000002212704,0.2011936,0.00001031395,0.001677673,0.2132326,0.3254192,0.08014341,0.002556451,0.001721244],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979115,0.0002906324,0.00005094555,0.0003241751,0.0000135227,0.000112741,0.00003050588,0.000007907063,0.001258059],"genre_scores_gemma":[0.9993345,0.00001249897,0.000148515,0.0001772344,0.000008614696,0.00001275138,0.000005101123,3.823641e-7,0.0003004708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6034771,"threshold_uncertainty_score":0.9977135,"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."}}