{"id":"W6944783229","doi":"10.2193/0022-541x(2005)069<0077:mrwhru>2.0.co;2","title":"MODELING REGIONAL WATERFOWL HARVEST RATES USING MARKOV CHAIN MONTE CARLO","year":2005,"lang":"en","type":"article","venue":"BioOne Complete (BioOne)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Waterfowl; Markov chain Monte Carlo; Monte Carlo method; Markov chain; Growing season; Markov model","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":[0.003680354,0.0004279418,0.0006745453,0.000863528,0.0004822605,0.0008946591,0.001420557,0.0008781386,0.001796884],"category_scores_gemma":[0.00843216,0.0007524582,0.0008598664,0.0007381703,0.0009682024,0.001198993,0.0005590234,0.001114872,0.000283889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001624131,"about_ca_system_score_gemma":0.0009781417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05869188,"about_ca_topic_score_gemma":0.04409544,"domain_scores_codex":[0.999413,0.0002510564,0.00002640668,0.0001751613,0.00006229272,0.00007204509],"domain_scores_gemma":[0.9948351,0.00397222,0.0004679405,0.0003084097,0.000297531,0.000118678],"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":[0.00001755454,0.000008513338,0.002588317,0.000003786658,0.00001772039,0.00001285494,0.0000176577,0.9935448,0.00009182996,0.001913661,0.00008683636,0.001696359],"study_design_scores_gemma":[0.000003819031,0.000003124529,0.0003926794,0.000001792891,0.00000324061,0.000002582218,0.000002664885,0.9984996,0.00004340687,0.00099461,0.00004943006,0.000002999095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4143068,0.0001717075,0.582094,0.0003074169,0.00002041382,0.00007765688,0.0008507763,0.0005889788,0.001582264],"genre_scores_gemma":[0.9554722,0.00009652571,0.04256807,0.0000433775,0.00001681081,0.0001129363,0.0006004815,0.00005923626,0.001030378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05869188,"threshold_uncertainty_score":0.1167005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3649452323350433,"score_gpt":0.2957068611403681,"score_spread":0.06923837119467519,"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."}}