{"id":"W7023607255","doi":"","title":"Optimising sample sizes for animal distribution analysis using tracking data","year":2021,"lang":"en","type":"article","venue":"UWA Profiles and Research Repository (UWA)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Range (aeronautics); Probabilistic logic; Sample size determination; Sample (material); Property (philosophy); Tracking (education); Distribution (mathematics); Statistical model","routes":{"ca_aff":true,"ca_fund":false,"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":[0.07824009,0.001405006,0.001894663,0.00273859,0.001466182,0.002162703,0.005434325,0.002784032,0.004095907],"category_scores_gemma":[0.2852111,0.001563685,0.002460128,0.002643617,0.002312694,0.003465959,0.004056401,0.00306296,0.001687581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001321961,"about_ca_system_score_gemma":0.002333927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004353779,"about_ca_topic_score_gemma":0.006979724,"domain_scores_codex":[0.9681711,0.02333793,0.002302344,0.00384069,0.001952639,0.0003953536],"domain_scores_gemma":[0.7686231,0.1972425,0.006265943,0.01794738,0.008758951,0.001162154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002913025,0.0006599355,0.09228649,0.002711826,0.002346063,0.0008598347,0.003062759,0.1781584,0.02176356,0.03018288,0.02803792,0.6370173],"study_design_scores_gemma":[0.001472612,0.0009457609,0.02558459,0.000766661,0.0005981011,0.0007221574,0.0006941413,0.8134781,0.01881524,0.11406,0.0226641,0.0001985623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01412085,0.0002365064,0.9810773,0.0003950179,0.0001243368,0.001176413,0.0007170981,0.001793828,0.0003586314],"genre_scores_gemma":[0.09831538,0.0000966198,0.8950799,0.0002984703,0.00009887278,0.003416097,0.001792967,0.0005100786,0.0003915306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07824009,"threshold_uncertainty_score":0.4137781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2145217805542298,"score_gpt":0.4587030829838253,"score_spread":0.2441813024295955,"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."}}