{"id":"W1995884763","doi":"10.1111/j.1538-4632.2000.tb00416.x","title":"Accuracy of Count Data Estimated by the Point‐in‐Polygon Method","year":2000,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Health Information","funders":"","keywords":"Polygon (computer graphics); Point (geometry); Weighting; Interpolation (computer graphics); Point in polygon; Distribution (mathematics); Mathematics; Algorithm; Geometry; Computer science; Monotone polygon; Mathematical analysis; Artificial intelligence; Image (mathematics)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001769691,0.0001726833,0.0007645863,0.0006516861,0.00009819867,0.00007478003,0.001172135,0.0001149543,0.008383504],"category_scores_gemma":[0.0002412356,0.0001359313,0.0004015316,0.0054717,0.0001252466,0.0002715823,0.0001388867,0.0002096889,0.0001636294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001276133,"about_ca_system_score_gemma":0.000008317763,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03985802,"about_ca_topic_score_gemma":0.002305302,"domain_scores_codex":[0.9979149,0.00006786919,0.0009293299,0.000658159,0.0001255311,0.0003042143],"domain_scores_gemma":[0.9976234,0.0003204391,0.0003259046,0.001603574,0.00004322899,0.00008341222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007936207,0.0003943082,0.9388554,0.00002125487,0.00368208,0.000006997451,0.0001454784,0.004300653,0.00003059336,0.005216165,0.004623015,0.04264469],"study_design_scores_gemma":[0.0006407557,0.00004751414,0.4299344,0.00001100909,0.001815915,0.00000423607,0.00005925499,0.4941377,0.0000404998,0.01433764,0.0584726,0.0004984757],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8368001,0.02081241,0.08981083,0.01726397,0.0001468188,0.0005955115,0.0203891,0.0001346616,0.01404656],"genre_scores_gemma":[0.9913415,0.003062026,0.001983107,0.0004273022,0.00002767288,0.00001234313,0.002790176,0.00001208334,0.000343827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.508921,"threshold_uncertainty_score":0.992523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04872628238713955,"score_gpt":0.2893462804185521,"score_spread":0.2406199980314125,"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."}}