{"id":"W3145226536","doi":"10.1145/3362741","title":"Help Me to Help You","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Social Computing","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Los Alamos National Laboratory; Science and Technology Facilities Council; Planetary Science Division; Science Mission Directorate; Smithsonian Astrophysical Observatory; Max-Planck-Institut für Astronomie; Queen's University; University of Edinburgh; Johns Hopkins University; Queen's University Belfast; National Aeronautics and Space Administration; Eötvös Loránd Tudományegyetem; National Central University; Central Laser Facility, Science and Technology Facilities Council; Space Telescope Science Institute; Durham University; Smithsonian Institution; National Science Foundation","keywords":"Citizen science; Leverage (statistics); MNIST database; Software deployment; Computer science; Competition (biology); Data science; Variety (cybernetics); Artificial intelligence; Machine learning; Deep learning; Software engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001391138,0.0001152635,0.0001259467,0.00002889559,0.0004261958,0.0000432176,0.0002756973,0.00006690724,0.03869316],"category_scores_gemma":[0.00001240848,0.0001235262,0.00009922729,0.0003628232,0.00004289835,0.00007616025,0.00003106459,0.0001518519,0.01154976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004167356,"about_ca_system_score_gemma":0.000005291194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001275472,"about_ca_topic_score_gemma":0.00007321995,"domain_scores_codex":[0.9989765,0.00003351848,0.0001470725,0.0002696858,0.0002771035,0.0002961316],"domain_scores_gemma":[0.999598,0.00004878954,0.00003701381,0.0002157172,0.00001039733,0.00009012228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002971849,0.001605701,0.04035915,0.00007904341,0.0001764773,0.00001777884,0.02396283,0.01138311,0.08889158,0.003193083,0.04133286,0.7887012],"study_design_scores_gemma":[0.003205993,0.0007213701,0.5984249,0.00008317515,0.0001056957,0.00002460066,0.04704473,0.001687593,0.02208081,0.0008175874,0.323552,0.002251544],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9616089,0.000003595485,0.01351061,0.002538361,0.0004813707,0.0002233263,0.00003255159,0.0001392598,0.02146203],"genre_scores_gemma":[0.9972283,0.000002623388,0.0003598438,0.001161229,0.00006431479,0.000005295133,0.000008646202,0.00001342401,0.001156374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7864497,"threshold_uncertainty_score":0.9892198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03797441798776483,"score_gpt":0.2828596139350032,"score_spread":0.2448851959472384,"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."}}