{"id":"W2511054836","doi":"10.1642/auk-16-92.1","title":"Trends, costs, benefits, challenges, and prognoses for supplementary materials","year":2016,"lang":"en","type":"article","venue":"The Auk","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Vetting; Reliability (semiconductor); Value (mathematics); Production (economics); Volume (thermodynamics); Computer science; Economics; Microeconomics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01182498,0.0001025269,0.0001907089,0.008470606,0.0002038776,0.0006199666,0.001229781,0.00004446106,0.001554416],"category_scores_gemma":[0.004714209,0.00004046076,0.00005271226,0.0118416,0.0001438273,0.0002121092,0.0004990048,0.0000337913,0.00007876624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003810421,"about_ca_system_score_gemma":0.00003056092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006412507,"about_ca_topic_score_gemma":0.00008345699,"domain_scores_codex":[0.9965243,0.0001357983,0.0003447724,0.0004288196,0.002138991,0.0004272602],"domain_scores_gemma":[0.9937095,0.005007118,0.0001158472,0.0005328965,0.0004826695,0.0001519769],"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":[0.00003328384,0.00002952642,0.001464856,0.000002409554,0.00001044024,6.404067e-7,0.00006086705,3.371825e-8,0.002273804,0.009831328,0.033789,0.9525038],"study_design_scores_gemma":[0.001418638,0.0004879521,0.1768958,0.00002504613,0.00001443625,0.00001141151,0.0005432483,0.00001585675,0.0235888,0.02407393,0.7726995,0.0002254059],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9117588,0.02001206,0.0001852641,0.06211702,0.0007767816,0.0007687444,0.002001368,0.00003126073,0.002348711],"genre_scores_gemma":[0.9879745,0.008287416,0.0003720988,0.0001580648,0.0001747976,0.00007857897,0.000008697739,0.00001063184,0.002935249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9522784,"threshold_uncertainty_score":0.9993583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6499792727979707,"score_gpt":0.5435429268533936,"score_spread":0.1064363459445772,"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."}}