{"id":"W4410827624","doi":"10.3897/aca.8.e151516","title":"Using big data to address global environmental challenges","year":2025,"lang":"en","type":"article","venue":"ARPHA Conference Abstracts","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Data science; Computer science; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002619767,0.000252175,0.0002261443,0.0001457607,0.000173977,0.0004231523,0.001392181,0.0001054517,0.0003286146],"category_scores_gemma":[0.0001485461,0.0002458017,0.00003239335,0.0003487548,0.00009312608,0.001542353,0.001379133,0.0001338625,0.0009076706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004861069,"about_ca_system_score_gemma":0.00008053622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008182172,"about_ca_topic_score_gemma":0.0007241098,"domain_scores_codex":[0.9982878,0.000007373368,0.0003327926,0.0006703715,0.000287283,0.0004143816],"domain_scores_gemma":[0.9986988,0.00003145406,0.0001407113,0.001031999,0.00006691017,0.00003009177],"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":[0.0001177843,0.0004887468,0.009846224,0.0003760952,0.0001183734,0.00007607064,0.00005470403,0.0004817219,0.006624235,0.04626326,0.009688658,0.9258641],"study_design_scores_gemma":[0.000494411,0.00001102047,0.3898404,0.0005595753,0.0001665537,0.00001035725,0.0007749607,0.003417465,0.0009260281,0.005859961,0.5970851,0.0008541267],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7922397,0.001637057,0.006950343,0.007194001,0.004319848,0.0008702836,0.0004096606,0.0003225033,0.1860566],"genre_scores_gemma":[0.996689,0.0001486681,0.0002814515,0.001792801,0.0006924382,0.000007884717,0.0002201606,0.00001196194,0.0001556347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.92501,"threshold_uncertainty_score":0.9999994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3777781153837068,"score_gpt":0.3587791297116659,"score_spread":0.01899898567204095,"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."}}