{"id":"W2517475355","doi":"10.1111/cag.12297","title":"Identifying the geographic extent of environmental inequalities: A comparison of pattern detection methods","year":2016,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Environmental Justice and Health Disparities","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Inequality; Cluster analysis; Key (lock); Geography; Data science; Computer science; Regional science; Data mining; Econometrics; Mathematics; Machine learning; Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001168587,0.0002851577,0.0004927755,0.001851517,0.00122511,0.00004559594,0.000625835,0.0002003008,0.0003069153],"category_scores_gemma":[0.0001521379,0.0002136015,0.0003696197,0.001509543,0.005386148,0.0003199923,0.0000660238,0.000184991,0.000003790793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000167589,"about_ca_system_score_gemma":0.0001447618,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7164959,"about_ca_topic_score_gemma":0.9890839,"domain_scores_codex":[0.9967071,0.0005844747,0.0007974723,0.000384015,0.0004810057,0.001045969],"domain_scores_gemma":[0.9976353,0.0007263945,0.0004423887,0.000494038,0.00005841528,0.0006434769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002346488,0.00007354264,0.7262592,0.0003009036,0.0002120802,0.000005232115,0.03065421,0.00000266717,0.001741007,0.007322205,0.0002054123,0.2332],"study_design_scores_gemma":[0.0004106563,0.0002246263,0.5978382,0.0003945755,0.0002135314,0.000004493626,0.3701471,0.000004567036,0.001500165,0.002391104,0.02636865,0.0005023276],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847084,0.01128734,0.0004009661,0.001148468,0.0007446045,0.0005358054,0.0003809266,0.00003893124,0.0007546038],"genre_scores_gemma":[0.9841598,0.01509689,0.0001821034,0.0002240812,0.0001040255,0.00008495168,0.000009633531,0.00002944423,0.0001090218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3394929,"threshold_uncertainty_score":0.9973206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02937664657248397,"score_gpt":0.3031086182507235,"score_spread":0.2737319716782395,"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."}}