{"id":"W4398334990","doi":"10.7910/dvn/pkjufn/rbyqva","title":"FCC2001.083.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":[0.00006154202,0.000448377,0.0004864514,0.00009774979,0.00006242967,0.0001121011,0.0006720021,0.0003690701,0.01586358],"category_scores_gemma":[0.00004188329,0.0004730423,0.0001383793,0.000190922,0.0000466512,0.0001691305,0.0001973036,0.0007408092,0.3291147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007250482,"about_ca_system_score_gemma":0.00002826222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000077422,"about_ca_topic_score_gemma":0.00006801022,"domain_scores_codex":[0.998507,0.00002798623,0.0003309681,0.0003991548,0.0002801899,0.0004547673],"domain_scores_gemma":[0.9985397,0.00004741291,0.00005869129,0.001071228,0.00002276633,0.0002602144],"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":[0.00001034984,0.00001358468,4.074406e-7,0.000242733,0.0001081042,0.0002236279,0.000009703126,0.002958645,0.00002131274,0.000007063556,0.9960299,0.0003746302],"study_design_scores_gemma":[0.000265104,0.00002232059,0.000004378466,0.00008620774,0.0001070028,0.00001314478,0.00001513538,0.003855839,0.00002206287,0.00000378668,0.9950882,0.0005168173],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000006734311,0.000006230161,0.00008616153,0.000004249222,0.00177011,0.0001785963,0.9967803,0.0003709113,0.0007967007],"genre_scores_gemma":[0.00003410953,0.001994936,0.000173848,0.0002998913,0.001435708,0.00001337222,0.9958493,0.00007921745,0.0001196273],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3132511,"threshold_uncertainty_score":0.9997721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118495727525685,"score_gpt":0.1999503156488169,"score_spread":0.1881007428962485,"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."}}