{"id":"W2422630017","doi":"10.1002/env.2396","title":"Multivariate density estimation for interval‐censored data with application to a forest fire modelling problem","year":2016,"lang":"en","type":"article","venue":"Environmetrics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Multivariate statistics; Estimator; Statistics; Density estimation; Interval estimation; Mathematics; Interval (graph theory); Estimation; Context (archaeology); Econometrics; Confidence interval; Geography; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007875853,0.0008782207,0.001586636,0.001656988,0.0005966051,0.001531699,0.002383187,0.001469061,0.003031611],"category_scores_gemma":[0.0342228,0.001022738,0.001618225,0.002653889,0.001200808,0.002175841,0.00247142,0.003258547,0.0004858065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001447714,"about_ca_system_score_gemma":0.001763076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01156011,"about_ca_topic_score_gemma":0.007127838,"domain_scores_codex":[0.9979073,0.001429886,0.00008582011,0.0002187025,0.0002643675,0.00009404566],"domain_scores_gemma":[0.9769323,0.02043119,0.0008530763,0.0005101637,0.00100906,0.0002642387],"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":[0.00007440637,0.00008402874,0.002631781,0.0002314885,0.0001401825,0.0002203873,0.0002720745,0.7676859,0.000545001,0.1560441,0.002936888,0.06913376],"study_design_scores_gemma":[0.000004611848,0.000007017128,0.0001731511,0.00001024689,0.000006225576,0.00002330437,0.00001175588,0.9775904,0.00007046773,0.02164441,0.0004503576,0.000007941694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00250992,0.0002235277,0.9967639,0.0001278216,0.000009775736,0.00001588692,0.00003550449,0.00005647,0.0002571167],"genre_scores_gemma":[0.3100266,0.003395297,0.6778564,0.0001600708,0.0002985661,0.0006206644,0.0009278915,0.0002788962,0.006435588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01156011,"threshold_uncertainty_score":0.04165202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084081056234413,"score_gpt":0.2470370524582049,"score_spread":0.2261962418958608,"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."}}