{"id":"W4398636197","doi":"10.7910/dvn/pkjufn/ahi23j","title":"FCC2001.333.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":[0.00006127027,0.0004483392,0.0004856447,0.0000977332,0.00006241633,0.0001134681,0.000671828,0.00036929,0.01592075],"category_scores_gemma":[0.00004101449,0.0004730996,0.0001388683,0.0001894923,0.00004664276,0.0001675774,0.0001972355,0.0007403328,0.3269208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007264355,"about_ca_system_score_gemma":0.0000280123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007795286,"about_ca_topic_score_gemma":0.00006741845,"domain_scores_codex":[0.9985091,0.00002766767,0.0003302178,0.000398964,0.0002797832,0.0004542612],"domain_scores_gemma":[0.9985426,0.00004707856,0.00005861689,0.001069548,0.00002254708,0.0002596066],"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":[0.00001033918,0.00001354143,3.489934e-7,0.0002419408,0.0001047899,0.0002903677,0.000009848068,0.002992369,0.00002020826,0.000007400175,0.9959356,0.000373225],"study_design_scores_gemma":[0.0002635193,0.00002180068,0.000004200233,0.00008479461,0.0001034884,0.0000178995,0.00001551984,0.003825855,0.00001833629,0.000003920011,0.9951237,0.0005169223],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000070529,0.000005883951,0.00008552636,0.000004251653,0.00176064,0.0001785305,0.9967636,0.0003715213,0.000822949],"genre_scores_gemma":[0.00004210731,0.001942734,0.0001749715,0.000301262,0.001430656,0.0000133645,0.9959016,0.0000792991,0.0001139575],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.311,"threshold_uncertainty_score":0.9997721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01184558204832753,"score_gpt":0.1998632451143943,"score_spread":0.1880176630660668,"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."}}