{"id":"W2786557781","doi":"10.1021/acsnano.7b08504","title":"Micromachined Chip Scale Thermal Sensor for Thermal Imaging","year":2018,"lang":"en","type":"article","venue":"ACS Nano","topic":"Thermal properties of materials","field":"Materials Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; International Institute for Nanotechnology, Northwestern University; Division of Electrical, Communications and Cyber Systems; Materials Research Science and Engineering Center, Harvard University; Division of Materials Research; Canada Research Chairs; Northwestern University; Division of Biological Infrastructure; W. M. Keck Foundation; National Science Foundation","keywords":"Scanning thermal microscopy; Materials science; Thermocouple; Cantilever; Nanotechnology; Optoelectronics; Nanowire; Silicon; Thermal; Microelectromechanical systems; Nanoscopic scale; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002034547,0.0003916919,0.00030464,0.0002575417,0.0001568664,0.0002768866,0.0008325391,0.0006245926,0.004250347],"category_scores_gemma":[0.0003882334,0.0002921361,0.0001973923,0.0002193833,0.000211341,0.0005069543,0.0002275251,0.0007910971,0.001810507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005618618,"about_ca_system_score_gemma":0.0004150979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005461249,"about_ca_topic_score_gemma":0.00176889,"domain_scores_codex":[0.999671,0.00002316624,0.00001245482,0.0001184811,0.0001507342,0.00002408605],"domain_scores_gemma":[0.9997736,0.00004687171,0.00003216113,0.00004524495,0.00008314331,0.00001901773],"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":[0.00002048002,0.00002050003,0.0001467112,0.00008394878,0.000008750893,0.00003672704,0.00001363547,0.0003973503,0.9883401,0.0006480854,0.001038551,0.00924514],"study_design_scores_gemma":[0.00001100748,0.000159077,0.001414984,0.000009608076,0.00001751989,0.0002629742,0.00001596903,0.01341288,0.9677365,0.0001897274,0.0167459,0.00002391107],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3585652,0.006160598,0.5814283,0.001016251,0.001608397,0.0006114673,0.002969379,0.008103786,0.03953664],"genre_scores_gemma":[0.5165628,0.001237673,0.46294,0.0004721027,0.00009526837,0.0003162668,0.0009467576,0.0001380882,0.01729118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004250347,"threshold_uncertainty_score":0.01421881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01318452464927689,"score_gpt":0.2431130933912238,"score_spread":0.2299285687419469,"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."}}