{"id":"W4387323451","doi":"10.48550/arxiv.2310.00346","title":"Fostering new Vertical and Horizontal IoT Applications with Intelligence Everywhere","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation; Cisco Systems","keywords":"Orchestration; Cloud computing; Computer science; Internet of Things; Collective intelligence; Context (archaeology); Data science; Knowledge management; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001830894,0.0005371246,0.0003547497,0.0006118669,0.001188157,0.004654374,0.00112225,0.001578569,0.002068857],"category_scores_gemma":[0.002319887,0.000363674,0.0005113286,0.0007661727,0.002311753,0.007615845,0.01042678,0.002620117,0.0007651636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006269756,"about_ca_system_score_gemma":0.001401461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008693943,"about_ca_topic_score_gemma":0.001559378,"domain_scores_codex":[0.9988134,0.0002914064,0.00005313649,0.0001956984,0.0003729837,0.000273389],"domain_scores_gemma":[0.9981043,0.0004043662,0.0001544688,0.0007363455,0.0002261967,0.0003743124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001301467,0.0003900243,0.00939999,0.0003841116,0.0001014487,0.0009552618,0.004175694,0.01404463,0.0445209,0.7097976,0.01557476,0.2005255],"study_design_scores_gemma":[0.00004445894,0.0002012577,0.004528366,0.0003644331,0.00007878811,0.0005800566,0.005075243,0.1274066,0.02220922,0.6342673,0.2051714,0.00007295867],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1947654,0.001917385,0.6133791,0.01397499,0.0004933837,0.0003013728,0.0001646587,0.002622698,0.1723811],"genre_scores_gemma":[0.8427408,0.001059263,0.1477915,0.001152035,0.0001863868,0.0001123087,0.000176233,0.0002054637,0.006576121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004654374,"threshold_uncertainty_score":0.009682775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1440397451167268,"score_gpt":0.2108435399060888,"score_spread":0.06680379478936205,"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."}}