{"id":"W6950268738","doi":"10.5281/zenodo.5256842","title":"D1.3: COLLABS Innovations for Industrial IoT Systems1","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Infineon Technologies (Canada)","funders":"European Commission","keywords":"Deliverable; Context (archaeology); Implementation; Section (typography); Internet of Things; Function (biology); Service provider; Service (business)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004680905,0.002018064,0.0005847076,0.00169248,0.001148633,0.006579968,0.003573542,0.002644686,0.08366437],"category_scores_gemma":[0.006804089,0.0007671045,0.001018817,0.001287005,0.001418346,0.004897449,0.006817336,0.003389152,0.05422467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002697314,"about_ca_system_score_gemma":0.003310124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003684377,"about_ca_topic_score_gemma":0.002656908,"domain_scores_codex":[0.9948983,0.0008079401,0.000200886,0.0004359236,0.003144915,0.0005119832],"domain_scores_gemma":[0.9973314,0.0005140314,0.0001490434,0.0006174805,0.000972444,0.0004156169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002134733,0.0002006458,0.0008993254,0.001684824,0.00005217054,0.0009652799,0.001868147,0.005959893,0.02499521,0.2047869,0.350547,0.4078272],"study_design_scores_gemma":[0.00001787095,0.0001239055,0.0003320135,0.0002483927,0.00001003667,0.0003152817,0.0001594564,0.002540203,0.005138184,0.01252493,0.9785574,0.00003231229],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006328825,0.003886789,0.4933466,0.006923357,0.002793436,0.001708126,0.004984536,0.0332339,0.4467945],"genre_scores_gemma":[0.06835538,0.007797585,0.3892398,0.004369083,0.001878578,0.003505949,0.02844484,0.01794889,0.4784599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08366437,"threshold_uncertainty_score":0.2798852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06769464149809694,"score_gpt":0.2385820326936601,"score_spread":0.1708873911955632,"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."}}