{"id":"W2157787224","doi":"10.1371/journal.pone.0134446","title":"A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Parks Canada","funders":"Parks Canada; Montana State University; Wilburforce Foundation; National Fish and Wildlife Foundation; Henry P. Kendall Foundation; Alberta Conservation Association","keywords":"Mark and recapture; Population; Grizzly Bears; Statistics; Population size; Abundance (ecology); Geography; Population density; Vital rates; Spatial analysis; Ecology; Biology; Demography; Mathematics; Population growth; Ursus","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004762384,0.0004373857,0.0005346116,0.0008513352,0.0002171265,0.0006992898,0.001470326,0.0004948086,0.0006971333],"category_scores_gemma":[0.01177694,0.0003049381,0.001158621,0.0005069388,0.0003803148,0.0009541797,0.0004648349,0.0003729472,0.0001549199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009055858,"about_ca_system_score_gemma":0.0007449765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02859441,"about_ca_topic_score_gemma":0.02021571,"domain_scores_codex":[0.9988655,0.0005550845,0.00007952,0.0002987549,0.0001282533,0.00007289979],"domain_scores_gemma":[0.9928027,0.005346166,0.0006831014,0.0004492351,0.0006132012,0.0001056438],"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":[0.0002892669,0.00008252546,0.2840408,0.0001421045,0.0005937375,0.0002445152,0.0004271623,0.6892139,0.001176256,0.002537711,0.00047202,0.02078003],"study_design_scores_gemma":[0.00003489183,0.0001268061,0.1019843,0.00004167797,0.000163458,0.0001647631,0.0001712745,0.894317,0.000514983,0.001913183,0.000511681,0.00005611461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839765,0.0004546507,0.01443456,0.000109895,0.00001079954,0.00001497568,0.0003317615,0.00007245318,0.0005945017],"genre_scores_gemma":[0.9939502,0.0001636897,0.004778517,0.00003318478,0.00000775361,0.00001958464,0.0007898717,0.00001694591,0.000240271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02859441,"threshold_uncertainty_score":0.05685592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07857120542174037,"score_gpt":0.2675463194103805,"score_spread":0.1889751139886401,"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."}}