{"id":"W2761305397","doi":"10.1109/ihtc.2017.8058178","title":"Maximizing the energy harvested from piezoelectric materials for clean energy generation","year":2017,"lang":"en","type":"article","venue":"","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Energy harvesting; Renewable energy; Electricity generation; Electric potential energy; Energy (signal processing); Computer science; Piezoelectricity; Production (economics); Environmental science; Automotive engineering; Power (physics); Process engineering; Electrical engineering; Engineering; Physics","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":[0.0002335563,0.0005000402,0.0004174351,0.0003989495,0.0003263185,0.000766627,0.0003343863,0.0004609731,0.004861642],"category_scores_gemma":[0.0003811785,0.0001792416,0.0002321379,0.0005460563,0.0002522144,0.001424135,0.0004030364,0.0004191585,0.001504633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002335483,"about_ca_system_score_gemma":0.0002612267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008503017,"about_ca_topic_score_gemma":0.0003664327,"domain_scores_codex":[0.9998389,0.00001416343,0.000005474275,0.00002840987,0.00008813412,0.00002484785],"domain_scores_gemma":[0.9998817,0.00006559947,0.00001284048,0.00001263521,0.00002302266,0.000004142541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007328295,0.00006967027,0.000446748,0.0005752179,0.00001877845,0.0001184579,0.00003648775,0.009378755,0.8780682,0.01505014,0.0007598759,0.09540445],"study_design_scores_gemma":[0.00001539124,0.0002344392,0.001726963,0.00006834466,0.00004288712,0.0002986915,0.0001077078,0.03402379,0.9315916,0.01128031,0.02058209,0.00002777294],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3073754,0.01365563,0.5951095,0.001110151,0.0003711628,0.0001540452,0.0005861321,0.0006056657,0.08103231],"genre_scores_gemma":[0.8857203,0.009839312,0.09052964,0.0001457167,0.00008829185,0.00008419124,0.0002640096,0.0002091985,0.01311943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004861642,"threshold_uncertainty_score":0.01626384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03562468220625459,"score_gpt":0.2347655683036742,"score_spread":0.1991408860974196,"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."}}