{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006254219,0.0002259068,0.000260129,0.0000400977,0.0003299451,0.0001926368,0.0005580083,0.0000693121,0.002216456],"category_scores_gemma":[0.00008508466,0.00017153,0.00007767585,0.00004427576,0.0002976189,0.0003299319,0.0001970709,0.00004809012,0.00239963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000345663,"about_ca_system_score_gemma":0.00004205438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001107107,"about_ca_topic_score_gemma":0.000005282846,"domain_scores_codex":[0.9984137,0.0001205471,0.0003139162,0.0003972625,0.0001815562,0.0005730307],"domain_scores_gemma":[0.9991134,0.00006906014,0.0001324856,0.0004768012,0.000123541,0.00008468387],"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":[0.0003185846,0.00003770101,0.000315636,0.0000221409,0.000006994942,0.000002099835,0.0005066855,0.00000356693,0.9960873,0.00002939621,0.0007548428,0.001915111],"study_design_scores_gemma":[0.0007032502,0.000100558,0.002209026,0.00002524736,0.00001848254,0.00001312969,0.00005181544,0.00003266663,0.9924022,0.00004740465,0.004138993,0.0002572114],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949269,0.00009892289,0.00008422699,0.0006217802,0.001303529,0.0004427998,0.00008184343,0.0002322742,0.002207667],"genre_scores_gemma":[0.9926211,0.000001006532,0.004125335,0.0008500915,0.001159725,0.00004485181,0.000007835445,0.0000644942,0.001125522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004041108,"threshold_uncertainty_score":0.9986957,"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."}}