{"id":"W4398427670","doi":"10.7910/dvn/pkjufn/azjszs","title":"FCC2001.098.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":[0.00006131281,0.0004480499,0.0004881146,0.00009763432,0.00006233317,0.000112212,0.0006708379,0.0003688678,0.01589634],"category_scores_gemma":[0.00004116061,0.0004729063,0.0001386361,0.0001890792,0.00004658354,0.0001690168,0.0001973764,0.0007398994,0.3292902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006981353,"about_ca_system_score_gemma":0.00002711096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007624784,"about_ca_topic_score_gemma":0.00006842853,"domain_scores_codex":[0.9985085,0.0000279194,0.000330657,0.0003988117,0.0002798153,0.0004543718],"domain_scores_gemma":[0.9985414,0.00004728446,0.00005864697,0.001070403,0.00002226723,0.0002599876],"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":[0.00001037933,0.00001399423,4.147492e-7,0.0002434065,0.0001052655,0.0002231554,0.000009349965,0.002834752,0.00001998121,0.000007454157,0.9961454,0.0003864287],"study_design_scores_gemma":[0.000266349,0.00002185488,0.000004423623,0.00008594768,0.0001040374,0.00001314913,0.00001523429,0.003763606,0.00002138379,0.000003955075,0.9951835,0.0005166022],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000006746154,0.000006412191,0.00008746958,0.000004211035,0.001765116,0.0001782603,0.9968082,0.0003707968,0.0007728026],"genre_scores_gemma":[0.00003242817,0.002037162,0.0001799933,0.0002960335,0.001423724,0.00001335181,0.9958191,0.00007915771,0.0001190531],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3133939,"threshold_uncertainty_score":0.9997723,"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."}}