{"id":"W3177956955","doi":"10.14236/ewic/eva2021.0","title":"EVA London 2021 - Index","year":2021,"lang":"en","type":"article","venue":"Electronic workshops in computing","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Computer science; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.003775737,0.0001616448,0.0003058153,0.0003077041,0.000181608,0.0003146901,0.000484103,0.0001286102,0.0004607918],"category_scores_gemma":[0.001539897,0.0001517447,0.0001329525,0.002888375,0.00004055993,0.0001687058,0.0001845767,0.0006463562,0.0001273321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001270269,"about_ca_system_score_gemma":0.0003198906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002396687,"about_ca_topic_score_gemma":0.0004353325,"domain_scores_codex":[0.9964138,0.0003578779,0.0007704992,0.0007128163,0.0009972475,0.0007477144],"domain_scores_gemma":[0.9974309,0.001443963,0.000194685,0.0005760289,0.0002820494,0.00007235625],"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":[0.00002985007,0.000123659,0.03852151,0.000004403405,0.00002359858,0.00007041705,0.0008109662,0.1586878,0.0002492172,0.009071008,0.001118429,0.7912892],"study_design_scores_gemma":[0.000790039,0.00003829655,0.01863346,0.0001203378,0.000009270701,0.00005196377,0.001808825,0.8971926,0.0002232171,0.07130474,0.00947938,0.000347888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8291566,0.00372207,0.1569153,0.0012297,0.0005976899,0.00009692619,6.233218e-7,0.00005662853,0.008224514],"genre_scores_gemma":[0.9974964,0.0001302168,0.000986813,0.0002729568,0.0001814862,0.00000279691,0.000008497208,0.00001558517,0.0009052674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7909412,"threshold_uncertainty_score":0.6187971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04790901841533868,"score_gpt":0.3596994618734922,"score_spread":0.3117904434581535,"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."}}