{"id":"W1893835876","doi":"10.1007/s00300-015-1737-5","title":"Estimating the abundance of the Southern Hudson Bay polar bear subpopulation with aerial surveys","year":2015,"lang":"en","type":"article","venue":"Polar Biology","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Trent University; Ministry of Natural Resources and Forestry","funders":"Ontario Ministry of Natural Resources and Forestry","keywords":"Transect; Bay; Abundance (ecology); Mark and recapture; Aerial survey; Distance sampling; Oceanography; Range (aeronautics); Ecology; Sampling (signal processing); Geography; Physical geography; Biology; Fishery; Population; Cartography; Demography; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008437066,0.00009813099,0.0001343153,0.000005700489,0.0001460423,0.00001083314,0.0003143274,0.00004693431,0.0001610595],"category_scores_gemma":[0.0002004495,0.00004823642,0.00003384759,0.0001524135,0.0003150713,0.00004999741,0.0003366967,0.00009980272,0.0001137115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004862143,"about_ca_system_score_gemma":0.00001157625,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02699689,"about_ca_topic_score_gemma":0.0189723,"domain_scores_codex":[0.998986,0.0003637915,0.0001534313,0.0001712005,0.0001369312,0.0001886575],"domain_scores_gemma":[0.999494,0.00005065258,0.0001532759,0.0002552704,0.00001406554,0.00003276026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000137018,0.00001208884,0.9948751,0.000002913416,0.000009504356,1.522725e-7,0.0004147835,0.0001764447,0.0005258388,0.00007238429,0.00008032237,0.003816706],"study_design_scores_gemma":[0.0002315912,0.000138834,0.9915641,0.000007760159,0.00001365459,0.000005086617,0.0001261714,0.0007646368,0.0001118746,0.000580281,0.006359253,0.00009674096],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965608,0.0001472873,0.0008258673,0.0005083382,0.000155108,0.0001821647,0.00003369672,0.00001319507,0.001573586],"genre_scores_gemma":[0.9988666,0.000002279613,0.0008225194,0.0001127333,0.00008335347,0.000004652108,0.000007591845,0.000009026428,0.00009120428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008024586,"threshold_uncertainty_score":0.9989289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02807078545186092,"score_gpt":0.2524991594174183,"score_spread":0.2244283739655574,"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."}}