{"id":"W2011278320","doi":"10.1111/j.1541-0420.2008.01070.x","title":"Inference for Clustered Inhomogeneous Spatial Point Processes","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Point processes and geometric inequalities","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cancer Care Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Engineering and Physical Sciences Research Council; Arnold Arboretum; National Institute for Environmental Studies; John D. and Catherine T. MacArthur Foundation; National Science Foundation","keywords":"Resampling; Point process; Inference; Nonparametric statistics; Cluster analysis; Computer science; Confidence interval; Poisson distribution; Statistics; Parametric statistics; Point estimation; Econometrics; Artificial intelligence; Machine learning; Data mining; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0003999627,0.0002270264,0.0003661937,0.00163641,0.0002360694,0.00005521365,0.0003281408,0.0001519044,0.00006750249],"category_scores_gemma":[0.01866666,0.0001968707,0.0001027492,0.005599422,0.00008638453,0.0001715352,0.0001131351,0.00009002351,0.00002709677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006946779,"about_ca_system_score_gemma":0.0003053379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008817506,"about_ca_topic_score_gemma":0.00003866971,"domain_scores_codex":[0.9982624,0.00002276717,0.0005055842,0.0003163158,0.0004459877,0.0004469658],"domain_scores_gemma":[0.9962013,0.002303953,0.000249764,0.0003064851,0.0008183626,0.0001201251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002518661,0.01168245,0.04373989,0.06188305,0.001641868,0.0007469605,0.03410162,0.0001360229,0.003984732,0.1891189,0.2193474,0.4310985],"study_design_scores_gemma":[0.0113465,0.005637633,0.003355183,0.0005059066,0.0004879979,0.001101671,0.002178064,0.003259211,0.09757808,0.5810952,0.2882737,0.005180829],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3436363,0.00170316,0.6502232,0.0002554208,0.0005968437,0.0008743571,0.000276079,0.0003239964,0.002110661],"genre_scores_gemma":[0.9720923,0.0004050415,0.02515171,0.0001497009,0.0003043939,0.00009491546,0.00003251312,0.00003934192,0.001730112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.628456,"threshold_uncertainty_score":0.9895995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1578492314649912,"score_gpt":0.3531639959523032,"score_spread":0.1953147644873121,"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."}}