{"id":"W4411885375","doi":"10.3390/jsan14040068","title":"Graphene–PLA Printed Sensor Combined with XR and the IoT for Enhanced Temperature Monitoring: A Case Study","year":2025,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Graphene; Internet of Things; Computer science; Embedded system; Real-time computing; Materials science; Nanotechnology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003074009,0.0002400085,0.0004889198,0.00009287445,0.0002071091,0.0001356463,0.00007821427,0.00008313303,0.000001528236],"category_scores_gemma":[0.00007440589,0.0001410112,0.00006429006,0.0001649304,0.00009170992,0.00008247744,0.0000216149,0.0003053118,5.62204e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001882584,"about_ca_system_score_gemma":0.00001267852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006823135,"about_ca_topic_score_gemma":0.000008703548,"domain_scores_codex":[0.9990244,0.00006970968,0.0004093713,0.0001490227,0.0001116077,0.0002358748],"domain_scores_gemma":[0.9989737,0.0004760837,0.0001389696,0.0001520608,0.0001603177,0.00009889808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.02228487,0.0006271396,0.008412055,0.001078712,0.005397683,0.004706707,0.01159293,0.731554,0.1950542,0.0007713821,0.0007981831,0.01772221],"study_design_scores_gemma":[0.2323878,0.0154313,0.03672639,0.009471454,0.008654232,0.04736191,0.1322113,0.182194,0.3178976,0.003915413,0.006250828,0.007497681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939052,0.000378663,0.004456979,0.00006235176,0.0006295277,0.0004725284,0.000002931912,0.00005133603,0.00004050728],"genre_scores_gemma":[0.9971343,0.0001920111,0.002122854,0.0000352252,0.0003584403,0.00001846699,4.994826e-7,0.00003164915,0.0001065363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.54936,"threshold_uncertainty_score":0.5750269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00714941394141825,"score_gpt":0.2322721219764692,"score_spread":0.2251227080350509,"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."}}