{"id":"W4319297836","doi":"10.1039/d2nr06382d","title":"Boosting the sensitivity with time-gated luminescence thermometry using a nanosized molecular cluster aggregate","year":2023,"lang":"en","type":"article","venue":"Nanoscale","topic":"Luminescence Properties of Advanced Materials","field":"Materials Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; University of Ottawa","keywords":"Luminescence; Boosting (machine learning); Aggregate (composite); Sensitivity (control systems); Cluster (spacecraft); Materials science; Aggregation-induced emission; Nanotechnology; Optoelectronics; Computer science; Fluorescence; Machine learning; Optics; Engineering; 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":[0.0003668236,0.0002700195,0.0001982745,0.0002682566,0.0001893206,0.0004950643,0.0005098811,0.0004646156,0.0008775335],"category_scores_gemma":[0.0005659863,0.0001549707,0.0002035391,0.0001981676,0.0004349596,0.0004852234,0.0005998011,0.0006218339,0.0003408002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005665101,"about_ca_system_score_gemma":0.0002268726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005163712,"about_ca_topic_score_gemma":0.0006346448,"domain_scores_codex":[0.9997926,0.00002598447,0.000009810247,0.00008211529,0.00006119646,0.00002833923],"domain_scores_gemma":[0.9997383,0.00007936769,0.0000648062,0.00003946037,0.00004213108,0.0000358806],"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":[0.00006730845,0.00002442524,0.0001632287,0.00004285422,0.000006757321,0.00002317211,0.00004581637,0.001538378,0.9931329,0.001412971,0.0001484738,0.003393669],"study_design_scores_gemma":[0.000008127422,0.00010843,0.0002268303,0.000003583431,0.000007492832,0.0000250095,0.0000115532,0.0136109,0.9846876,0.0001665638,0.001130546,0.00001336937],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9464905,0.0006973284,0.04868764,0.0004121707,0.0000951261,0.00005232891,0.0001192599,0.0005974209,0.002848247],"genre_scores_gemma":[0.9772123,0.000310923,0.02085517,0.00009345677,0.00001918824,0.00003853967,0.00006262477,0.00005745851,0.001350333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008775335,"threshold_uncertainty_score":0.004110336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01478245484859281,"score_gpt":0.2444346484529414,"score_spread":0.2296521936043486,"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."}}