{"id":"W2335906319","doi":"10.5430/jbei.v2n2p109","title":"Characterizing the thermal ablation of cells for silicon biosensors","year":2016,"lang":"en","type":"article","venue":"Journal of Biomedical Engineering and Informatics","topic":"thermodynamics and calorimetric analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Biosensor; Materials science; Chip; Silicon; Silicon on insulator; Thermal; Ablation; Optoelectronics; Nanotechnology; Temperature measurement; Electrical engineering; Engineering; Thermodynamics","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":[0.000244211,0.00006114657,0.0001446824,0.00007960211,0.00001983732,0.000010785,0.0000919233,0.00005969779,0.00001792746],"category_scores_gemma":[0.0001263584,0.00002900833,0.00007388258,0.00007192518,0.00004534456,0.00009766782,0.00001427948,0.00006961449,3.799414e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001574264,"about_ca_system_score_gemma":0.00001631851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.34622e-7,"about_ca_topic_score_gemma":1.314402e-8,"domain_scores_codex":[0.9992778,0.000002038312,0.0004554655,0.00001969926,0.0001537998,0.00009120218],"domain_scores_gemma":[0.999293,0.0002298725,0.0002962836,0.00006080555,0.00006115969,0.00005889505],"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":[0.00002135377,0.00001702719,0.00003581137,0.000173752,0.00008220799,3.859247e-7,0.000394606,0.0001174431,0.9520356,0.0001488393,0.00003888041,0.04693408],"study_design_scores_gemma":[0.002806481,0.0004238824,0.00168958,0.000919706,0.0002457237,0.00008338239,0.001098561,0.3958574,0.5520687,0.00009730393,0.04434422,0.0003651389],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9792508,0.00007640229,0.02026684,0.0001809068,0.0001079123,0.00001723129,0.00001443783,0.000005377262,0.00008005967],"genre_scores_gemma":[0.9988953,0.000151494,0.0007670012,0.00001730134,0.0001355446,4.685932e-7,0.000001107563,0.000005390453,0.00002633644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.399967,"threshold_uncertainty_score":0.1182925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00617513835486377,"score_gpt":0.200178133386208,"score_spread":0.1940029950313442,"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."}}