{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008096533,0.0007999548,0.0006876405,0.001475357,0.0008164797,0.007073199,0.002114299,0.001159886,0.005147136],"category_scores_gemma":[0.028726,0.0004911199,0.0008025588,0.002060752,0.003171608,0.009262992,0.002655457,0.001662586,0.0008454907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001541073,"about_ca_system_score_gemma":0.00282706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01116984,"about_ca_topic_score_gemma":0.006583291,"domain_scores_codex":[0.9949678,0.00233636,0.0002205496,0.0004606298,0.00165097,0.0003638321],"domain_scores_gemma":[0.977099,0.01202911,0.001079527,0.005837128,0.003089156,0.0008660628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001987634,0.0009728409,0.2098057,0.0004556527,0.0005618517,0.0005544359,0.0009334161,0.1488553,0.01481667,0.1599297,0.02898985,0.432137],"study_design_scores_gemma":[0.00008815336,0.0003744105,0.02898703,0.000182371,0.00008301065,0.0001557633,0.00150312,0.7899668,0.02250201,0.144014,0.01207349,0.00006988591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6748052,0.001384247,0.2410752,0.02030362,0.0007867317,0.000416658,0.002329276,0.005213729,0.0536853],"genre_scores_gemma":[0.9594343,0.0002905383,0.03706289,0.000355129,0.00009821611,0.00005531352,0.0008181852,0.0002578621,0.001627643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01116984,"threshold_uncertainty_score":0.04281908,"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."}}