{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004733127,0.0004813301,0.0004981124,0.0002939969,0.0004456461,0.0003564149,0.001377654,0.0001555741,0.00002842697],"category_scores_gemma":[0.00005309768,0.0004498317,0.0002142257,0.000592852,0.000149132,0.00168213,0.000680597,0.0003431814,0.00008881069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002056508,"about_ca_system_score_gemma":0.00009716983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000376544,"about_ca_topic_score_gemma":0.00005236812,"domain_scores_codex":[0.9968564,0.0001383114,0.0006646489,0.000957794,0.000533824,0.0008490405],"domain_scores_gemma":[0.9981211,0.00008555645,0.0001843602,0.001071418,0.0002803572,0.0002571754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000454383,0.001121712,0.001697261,0.0003376314,0.0003927315,0.0003852118,0.002864333,0.06408837,0.5910283,0.02350496,0.01278003,0.3013451],"study_design_scores_gemma":[0.0005296101,0.0001137421,0.0001832742,0.0001499719,0.00001427783,0.00009741128,0.00002937138,0.9488799,0.02427555,0.0009114265,0.02411666,0.0006988237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0826368,0.001462126,0.9123344,0.002023615,0.0001646123,0.0003992245,0.00002529043,0.0007348787,0.0002190208],"genre_scores_gemma":[0.665034,0.0001932021,0.3326111,0.001128638,0.0004606338,0.00001807029,0.00001547578,0.00004799526,0.0004908636],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8847915,"threshold_uncertainty_score":0.9997953,"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."}}