{"id":"W4299614488","doi":"10.48550/arxiv.1310.2888","title":"Transdimensional Approximate Bayesian Computation for Inference on\\n Invasive Species Models with Latent Variables of Unknown Dimension","year":2013,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Approximate Bayesian computation; Latent variable; Inference; Bayesian inference; Bayesian probability; Sampling (signal processing); Computation; Dimension (graph theory); Computer science; Statistics; Mathematics; Ecology; Artificial intelligence; Algorithm; Biology","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":[0.007518736,0.001207596,0.001845595,0.002257554,0.0013116,0.002335052,0.002681118,0.001746075,0.00611248],"category_scores_gemma":[0.04258166,0.001596198,0.001893889,0.00240379,0.002760042,0.003148138,0.003253844,0.003595211,0.001157931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003527682,"about_ca_system_score_gemma":0.003717026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02150191,"about_ca_topic_score_gemma":0.03100737,"domain_scores_codex":[0.9973037,0.001596906,0.0001680721,0.0003499888,0.0004512894,0.0001299754],"domain_scores_gemma":[0.9776739,0.01915583,0.0006849646,0.001085234,0.00106308,0.0003371225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001394001,0.00009187354,0.002737427,0.0001927846,0.0001269654,0.0001024341,0.0002415514,0.7317244,0.0005317849,0.1955611,0.001835453,0.06671482],"study_design_scores_gemma":[0.00001021522,0.000006131002,0.0001020802,0.00001357137,0.000006111592,0.00001045144,0.000009890864,0.9484051,0.0001001801,0.05097865,0.0003504092,0.000007213979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005784452,0.0001995192,0.9928527,0.0001536129,0.000017975,0.00003971427,0.00009373766,0.0002998635,0.0005583542],"genre_scores_gemma":[0.1704524,0.0006975429,0.8236575,0.0002131622,0.0001198643,0.0006998337,0.001179939,0.0003223382,0.002657384],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02150191,"threshold_uncertainty_score":0.04275346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09589094610777238,"score_gpt":0.2093749556162374,"score_spread":0.113484009508465,"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."}}