{"id":"W2528091958","doi":"","title":"The pigeon as particle filter","year":2007,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Statistical Mechanics and Entropy","field":"Physics and Astronomy","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Particle filter; Bayesian inference; Bayesian probability; Monte Carlo method; Inference; Computer science; Asymptote; Artificial intelligence; Sampling (signal processing); Ensemble learning; Machine learning; Posterior probability; Importance sampling; Filter (signal processing); Algorithm; Statistics; Mathematics; Kalman filter; Computer vision","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":[0.0002735858,0.00007242071,0.00006528804,0.00001460193,0.0003384216,0.0004331534,0.00009322573,0.00001780898,0.00003763293],"category_scores_gemma":[0.00001837053,0.00004624951,0.00002347323,0.0001026108,0.00001885085,0.0005536257,0.00001736804,0.00008071459,0.000220607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001319638,"about_ca_system_score_gemma":0.00001744893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009091875,"about_ca_topic_score_gemma":3.070266e-7,"domain_scores_codex":[0.9992087,0.0000121855,0.0003162,0.00005220013,0.0001767005,0.000233965],"domain_scores_gemma":[0.9995446,0.0000671928,0.0001320879,0.00009305411,0.00009865555,0.00006436039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002341817,0.00001034607,0.001184797,0.00002810902,0.000007864114,3.992244e-7,0.0004676192,0.0001594914,0.00009918954,0.6800942,0.001373962,0.3165506],"study_design_scores_gemma":[0.0005509997,0.00008229187,0.001361959,0.00005583457,0.00001508163,0.000007780592,0.004087555,0.8430899,0.002494592,0.005470637,0.1425361,0.0002473476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2367752,0.0001501863,0.7179197,0.0005846095,0.001416807,0.0004210379,0.0000172455,0.0001255082,0.04258968],"genre_scores_gemma":[0.9991806,4.132422e-7,0.00004109402,0.0001251975,0.0001737908,0.00001538167,0.00001218542,0.000004428327,0.0004468933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8429304,"threshold_uncertainty_score":0.4176908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118451303110555,"score_gpt":0.2643726841054829,"score_spread":0.2525275537944274,"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."}}