{"id":"W4398548231","doi":"10.7910/dvn/pkjufn/vnfahe","title":"FCC2001.029.ran","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Wireless Sensor Networks and IoT","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Ran; Computer science; Computer network","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006162104,0.0004483159,0.0004864519,0.00009776707,0.00006244025,0.000112126,0.0006722176,0.0003689902,0.01653724],"category_scores_gemma":[0.00004191511,0.0004729567,0.0001383678,0.0001905017,0.00004662537,0.0001682926,0.0001973278,0.0007420928,0.3323725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006960697,"about_ca_system_score_gemma":0.00002742596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007516093,"about_ca_topic_score_gemma":0.00006766606,"domain_scores_codex":[0.9985071,0.00002798993,0.0003309089,0.0003990951,0.0002804206,0.000454518],"domain_scores_gemma":[0.9985402,0.00004735384,0.00005863016,0.00107098,0.00002278277,0.0002601195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001034987,0.00001398112,4.249137e-7,0.0002430741,0.000105126,0.000223115,0.000009715925,0.0028458,0.00002047496,0.000007101691,0.996133,0.0003878483],"study_design_scores_gemma":[0.0002654687,0.00002218306,0.000004485848,0.00008610648,0.0001034539,0.00001315766,0.00001547097,0.003747254,0.00002155479,0.000003783822,0.9952004,0.0005167137],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000006727564,0.00000610788,0.00008651946,0.000004353377,0.001732082,0.0001782711,0.9968073,0.000371054,0.0008076142],"genre_scores_gemma":[0.00003342165,0.002006676,0.0001737174,0.0002931013,0.00144908,0.00001335527,0.9958278,0.00007921225,0.0001236504],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3158352,"threshold_uncertainty_score":0.9997722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178758260523582,"score_gpt":0.2000781986591693,"score_spread":0.1882906160539335,"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."}}