{"id":"W4319842633","doi":"10.1111/ecog.06442","title":"Are trapping data suited for home‐range estimation?","year":2023,"lang":"en","type":"article","venue":"Ecography","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia","funders":"","keywords":"Estimator; Range (aeronautics); Home range; Kernel density estimation; Statistics; Leverage (statistics); Interpolation (computer graphics); Mathematics; Computer science; Ecology; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002734704,0.00006302891,0.00007197882,0.00008472686,0.0001792395,0.00001670996,0.0002601366,0.00004956698,0.0003553518],"category_scores_gemma":[0.00006778445,0.00006484079,0.00003875748,0.0007303064,0.00006479007,0.0002692835,0.00009579004,0.00004287107,0.0005356767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009394568,"about_ca_system_score_gemma":0.00000274114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001681876,"about_ca_topic_score_gemma":0.0002029691,"domain_scores_codex":[0.999379,0.00001874753,0.0001080276,0.0002412443,0.00007954287,0.0001734056],"domain_scores_gemma":[0.9994779,0.0001141929,0.00007233002,0.0002968782,0.000004267238,0.00003446634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005932369,0.00001692261,0.8693294,0.000006379937,0.00001150199,0.000001708135,0.00005694187,0.0002518481,0.00001709322,0.00002969505,0.126913,0.00335958],"study_design_scores_gemma":[0.0001965108,0.00001302787,0.9623773,0.000004903807,0.00001136628,5.802469e-7,0.00005886297,0.02278671,0.000005212443,0.002181277,0.01227944,0.00008481108],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994037,0.00001365503,0.002203271,0.002473793,0.0002189816,0.000260816,0.00008754817,0.0001788114,0.0005261018],"genre_scores_gemma":[0.9962133,0.00001069139,0.001854569,0.001055892,0.00004784636,0.00009923879,0.0004303867,0.0000112473,0.0002768673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1146336,"threshold_uncertainty_score":0.6885219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05321057478290084,"score_gpt":0.2772470939073889,"score_spread":0.224036519124488,"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."}}