{"id":"W3171172362","doi":"10.1002/ecs2.3560","title":"Estimating the proportion of a beluga population using specific areas from connectivity patterns and abundance indices","year":2021,"lang":"en","type":"article","venue":"Ecosphere","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Habitat; Geography; Population; Abundance (ecology); Ecology; Range (aeronautics); Wildlife; Estuary; Aerial survey; Critical habitat; Environmental science; Fishery; Physical geography; Cartography; Biology; Endangered species","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001132611,0.00007124992,0.0001149062,0.000001776855,0.0001446724,0.00002334628,0.00005854392,0.00002226619,0.002639716],"category_scores_gemma":[0.00004124005,0.0000557537,0.00002206826,0.00009375176,0.00004307774,0.0001420806,0.0002143358,0.00005985468,0.00001485042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006107176,"about_ca_system_score_gemma":0.00000366474,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01654663,"about_ca_topic_score_gemma":0.0218711,"domain_scores_codex":[0.9993491,0.00004382111,0.0001498963,0.0001992634,0.0001656149,0.00009228569],"domain_scores_gemma":[0.9996387,0.00004389011,0.0001590845,0.0001304138,0.00000855601,0.00001938438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003181431,0.00002225974,0.9570141,0.00001858201,0.000008509556,0.000003400889,0.0002008355,0.0006574276,0.0004368475,0.00001674745,0.0000790273,0.04153908],"study_design_scores_gemma":[0.00007789988,0.00001177564,0.9915824,0.00004783741,0.00001112048,0.00000369742,0.0001264403,0.006742086,0.0005155114,0.0005888511,0.0002275839,0.00006479047],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969629,0.0004025924,0.0008071801,0.00007609553,0.0000560262,0.0001100013,0.000009167572,0.000009649512,0.001566382],"genre_scores_gemma":[0.996089,0.0000378994,0.003762764,0.00003277578,0.00004095959,0.000003434641,0.000007082181,0.000005909626,0.00002022197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04147429,"threshold_uncertainty_score":0.998272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02129325822646556,"score_gpt":0.2417518343778371,"score_spread":0.2204585761513715,"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."}}