{"id":"W2512799421","doi":"","title":"Exploiting Big Earth Data: Computation, Testing, and CyberGIS III","year":2015,"lang":"en","type":"article","venue":"2015 AGU Fall Meeting","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Esri (Canada)","funders":"","keywords":"Computer science; Big data; Earth (classical element); Data mining; 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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003441372,0.0001450145,0.000229406,0.0001498006,0.0003075605,0.0004544689,0.001218632,0.00008917522,0.000002101431],"category_scores_gemma":[0.01430107,0.0001158291,0.00001819144,0.0008516607,0.0001570611,0.0003789404,0.001576816,0.0001756955,0.000171634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001457428,"about_ca_system_score_gemma":0.00008087565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878115,"about_ca_topic_score_gemma":0.001201916,"domain_scores_codex":[0.9976111,0.00007770478,0.0005717297,0.0007049987,0.0007353559,0.0002991245],"domain_scores_gemma":[0.996507,0.001384768,0.0003606159,0.001070475,0.0005031328,0.0001739806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008379564,0.00006123091,0.1097989,0.000007256471,0.00001787802,0.00000958994,0.0006984681,0.0005872371,0.00112724,0.001762744,0.1692286,0.7166925],"study_design_scores_gemma":[0.002443424,0.0002135045,0.02889162,0.000248033,0.00006471962,0.0001324027,0.02498585,0.1982079,0.0007129452,0.0787725,0.6640356,0.00129157],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9604999,0.0007367674,0.01941283,0.002932861,0.0003127484,0.0002850301,0.0001139137,0.000394234,0.01531165],"genre_scores_gemma":[0.9219775,0.0000173178,0.07720663,0.0001757275,0.000218801,0.00001499331,0.00004447508,0.00001552046,0.0003290245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7154009,"threshold_uncertainty_score":0.9940019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4748088503459253,"score_gpt":0.4043836811288045,"score_spread":0.07042516921712083,"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."}}