{"id":"W4287244254","doi":"10.48550/arxiv.2104.00634","title":"Security and Machine Learning Adoption in IoT: A Preliminary Study of\\n IoT Developer Discussions","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Internet of Things; Computer science; World Wide Web; Computer security; Resource (disambiguation); 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"],"consensus_categories":[],"category_scores_codex":[0.001005876,0.0006276628,0.0009076262,0.0006503334,0.0005955635,0.0002500718,0.0009506652,0.0004332119,0.00004229912],"category_scores_gemma":[0.0001805864,0.0006806613,0.0001960754,0.001813906,0.0002452224,0.0003003716,0.00460088,0.001952893,0.00000625904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002650499,"about_ca_system_score_gemma":0.000355788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001824309,"about_ca_topic_score_gemma":0.001440726,"domain_scores_codex":[0.9948774,0.001351233,0.0006904991,0.002242699,0.0002305511,0.0006076383],"domain_scores_gemma":[0.9972761,0.000291465,0.0005794263,0.001244983,0.0003125719,0.0002954358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002653007,0.002179014,0.2401676,0.0004061982,0.0001815437,0.001886667,0.05873561,0.6881099,0.0001758013,0.001072823,0.00000386532,0.006815684],"study_design_scores_gemma":[0.001820369,0.0007223626,0.1102904,0.001316845,0.0001990265,0.00005178973,0.03037783,0.853832,0.000120328,0.0003785805,0.00003967476,0.0008507615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9521377,0.0003555677,0.0460628,0.0000577462,0.0003729122,0.0007127768,0.000004695181,0.00008307953,0.00021273],"genre_scores_gemma":[0.9984612,0.0003089623,0.0006154597,0.00001690481,0.00003844508,0.000002056679,0.0000120265,0.00003418792,0.0005107707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1657222,"threshold_uncertainty_score":0.9995645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0560549084329281,"score_gpt":0.1883316075603427,"score_spread":0.1322766991274146,"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."}}