{"id":"W2066575046","doi":"10.1149/06401.0019ecst","title":"Miniaturization of Photothermal Cantilever Deflection Spectroscopy with an Electrical Readout","year":2014,"lang":"en","type":"article","venue":"ECS Transactions","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Calgary","funders":"Canada Excellence Research Chairs, Government of Canada; Canada Research Chairs; University of Calgary","keywords":"Cantilever; Miniaturization; Piezoresistive effect; Photothermal therapy; Deflection (physics); Materials science; Optoelectronics; Nanotechnology; Spectroscopy; Photothermal effect; Infrared; Optics; Physics","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":[0.0004337378,0.0004280226,0.0003507133,0.0002548001,0.0001400157,0.0003951495,0.0009991042,0.0005800232,0.001368292],"category_scores_gemma":[0.0007041483,0.0003395239,0.0002643306,0.000214704,0.000340199,0.0005545671,0.0005426197,0.0008599415,0.0004761265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003358454,"about_ca_system_score_gemma":0.0001890675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000247268,"about_ca_topic_score_gemma":0.0004740068,"domain_scores_codex":[0.9994375,0.00004468047,0.00002883517,0.0001445003,0.0003110462,0.00003337183],"domain_scores_gemma":[0.9996575,0.0001069274,0.00005234646,0.00007208726,0.00008857968,0.00002263594],"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":[0.000009330559,0.0000118847,0.0000933765,0.00004800696,0.000003793386,0.00003708933,0.0000134458,0.0002051688,0.9931938,0.0004827547,0.0001991707,0.005702257],"study_design_scores_gemma":[0.000007516463,0.0001144474,0.001122602,0.000009765208,0.0000106824,0.0002598861,0.00001257692,0.0101199,0.9828992,0.0002164915,0.005210553,0.00001650488],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4301172,0.005133235,0.5477332,0.002154251,0.0009148041,0.0004505439,0.0007746768,0.002472438,0.01024959],"genre_scores_gemma":[0.6101024,0.001600505,0.3834767,0.0004002509,0.000149894,0.0003873605,0.0002596499,0.00006989376,0.003553415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001368292,"threshold_uncertainty_score":0.004577339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005971883301270978,"score_gpt":0.240907671033539,"score_spread":0.234935787732268,"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."}}