{"id":"W4398530025","doi":"10.7910/dvn/pkjufn/ztk398","title":"FCC2002.333.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":[0.00006140881,0.0004411728,0.0004768657,0.00006248123,0.00006144983,0.0001124655,0.0006635788,0.0003596646,0.01548856],"category_scores_gemma":[0.00004759377,0.000464937,0.0001379787,0.0001779002,0.00004510886,0.0001642628,0.0001968157,0.0007303626,0.285149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000715184,"about_ca_system_score_gemma":0.00002178635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004855955,"about_ca_topic_score_gemma":0.00006112695,"domain_scores_codex":[0.9984754,0.0000275278,0.0003255162,0.0003909579,0.0003421472,0.0004384117],"domain_scores_gemma":[0.9985656,0.00004990281,0.00005759709,0.001055686,0.00001944515,0.0002517903],"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":[0.00001042366,0.00001326584,3.720604e-7,0.0002411868,0.0001025941,0.0002856638,0.000009516071,0.002478086,0.00002159622,0.000006093611,0.9963388,0.0004924432],"study_design_scores_gemma":[0.0002522467,0.00002176962,0.000004336195,0.00008401489,0.0001025777,0.00001712647,0.00001541368,0.003946851,0.00002378435,0.000003078853,0.9950203,0.0005085026],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000006866109,0.000005412604,0.00009368847,0.000003934779,0.001664581,0.0001725545,0.9968888,0.0003425469,0.0008215749],"genre_scores_gemma":[0.00005105388,0.001593346,0.0001733115,0.0002908162,0.0013832,0.00001339902,0.9962957,0.00007823469,0.0001209691],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2696605,"threshold_uncertainty_score":0.9997802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01186855000602602,"score_gpt":0.1982685848667247,"score_spread":0.1864000348606986,"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."}}