{"id":"W2626942767","doi":"10.17713/ajs.v27i1&2.530","title":"Higher Order Cumulants and Inference for a Class of Filtered Poisson Processes","year":2016,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Mathematics; Cumulant; Estimator; Poisson distribution; Point process; Applied mathematics; Gaussian; Bootstrapping (finance); Statistical physics; Algorithm; Statistics; Econometrics; Physics","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":[0.009498275,0.0008197172,0.0009972408,0.004100706,0.001016738,0.002136133,0.001808724,0.001547631,0.003009381],"category_scores_gemma":[0.03457226,0.0005377254,0.001945856,0.001988214,0.002616863,0.003063795,0.001422433,0.002602262,0.0004740628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002069267,"about_ca_system_score_gemma":0.001904846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007620683,"about_ca_topic_score_gemma":0.005373223,"domain_scores_codex":[0.9977434,0.0007660961,0.0001286218,0.0004933408,0.0006742032,0.0001944857],"domain_scores_gemma":[0.9795842,0.01621026,0.001631271,0.001087814,0.001194992,0.000291572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004462231,0.00006593548,0.003522718,0.0001569884,0.0001137397,0.0002191827,0.0002920655,0.121358,0.002732203,0.8239061,0.001297598,0.04629088],"study_design_scores_gemma":[0.000008396964,0.00001873878,0.001522281,0.00003037853,0.00001992121,0.0001046287,0.00002850706,0.6847215,0.0007599425,0.311208,0.001539815,0.00003792904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01150778,0.0004165018,0.9865174,0.0002601658,0.00004049438,0.00002878954,0.0001190679,0.0001181925,0.0009916467],"genre_scores_gemma":[0.4953139,0.003868634,0.4871169,0.0004920528,0.0008458271,0.0004204875,0.001169691,0.0003032305,0.01046927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009498275,"threshold_uncertainty_score":0.05023223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2619669503074761,"score_gpt":0.5566710602753584,"score_spread":0.2947041099678823,"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."}}