{"id":"W3183701810","doi":"10.1002/env.2697","title":"A self‐exciting marked point process model for drought analysis","year":2021,"lang":"en","type":"article","venue":"Environmetrics","topic":"Point processes and geometric inequalities","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Quantile; Generalized Pareto distribution; Econometrics; Inference; Point process; Extreme value theory; Pareto principle; Return period; Environmental science; Computer science; Statistics; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0009074401,0.000239324,0.0005314493,0.000908635,0.0001900569,0.00009092325,0.0002367082,0.0001527608,0.0001786088],"category_scores_gemma":[0.00444589,0.0002353632,0.0003715242,0.005520694,0.00002976442,0.0002213399,0.0001112365,0.000173833,0.0000128102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001092986,"about_ca_system_score_gemma":0.00009477367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002693499,"about_ca_topic_score_gemma":0.000006836894,"domain_scores_codex":[0.9979473,0.00005017157,0.0005561398,0.0004761142,0.0004888771,0.0004814191],"domain_scores_gemma":[0.9975051,0.001488754,0.0002619737,0.0004629845,0.0001659443,0.0001152322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004956429,0.01154418,0.02555606,0.02945868,0.02103257,0.0004446911,0.05354311,0.1058197,0.0006433934,0.6651752,0.02241035,0.06387646],"study_design_scores_gemma":[0.0007616403,0.00004634225,0.0001510359,0.00001933009,0.001559453,0.00001542512,0.001471903,0.7937362,0.001888263,0.1969608,0.00283047,0.0005591845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0979465,0.0007931064,0.8964613,0.0002409798,0.00006012255,0.0002593596,0.0001125417,0.0001425682,0.003983515],"genre_scores_gemma":[0.754343,0.000253487,0.2349704,0.0002639075,0.0001388508,0.0001124292,0.00008497055,0.00006272128,0.009770265],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6879165,"threshold_uncertainty_score":0.9597834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05580067344120691,"score_gpt":0.3169298621448963,"score_spread":0.2611291887036893,"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."}}