{"id":"W3191600514","doi":"10.1103/physrevlett.127.071101","title":"Low Mechanical Loss <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>TiO</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo>:</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>GeO</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math> Coatings for Reduced Thermal Noise in Gravitational Wave Interferometers","year":2021,"lang":"lv","type":"article","venue":"Physical Review Letters","topic":"Pulsars and Gravitational Waves Research","field":"Physics and Astronomy","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Gordon and Betty Moore Foundation; National Science Foundation","keywords":"Noise (video); Sensitivity (control systems); Interferometry; LIGO; Algorithm; Physics; Materials science; Optics; Detector; Computer science; Artificial intelligence; Electronic engineering","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":[0.0005370979,0.0008186186,0.0004890554,0.0009991147,0.0005356123,0.001776836,0.001469372,0.0009007037,0.07153635],"category_scores_gemma":[0.001958782,0.0005625134,0.0003786682,0.000723908,0.000324788,0.001439447,0.0007177024,0.0009570624,0.04613412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004475,"about_ca_system_score_gemma":0.0004870969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00224724,"about_ca_topic_score_gemma":0.002865429,"domain_scores_codex":[0.9994412,0.00002754348,0.00002638466,0.00005533874,0.0004014069,0.00004818484],"domain_scores_gemma":[0.9990848,0.0001320039,0.0001209166,0.0002224268,0.0003882908,0.00005154464],"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":[0.0003417171,0.0001426523,0.0008405933,0.0006539907,0.00003677759,0.0002728564,0.0001473273,0.002420172,0.8129432,0.008452008,0.07859839,0.09515035],"study_design_scores_gemma":[0.0001219855,0.000406609,0.005792741,0.00008507857,0.00004583622,0.0004844893,0.0001001111,0.01372328,0.6625637,0.001686632,0.3149018,0.0000877893],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.187861,0.003876548,0.3892204,0.004410869,0.00286268,0.0007213735,0.01344917,0.04851197,0.349086],"genre_scores_gemma":[0.4348333,0.00290695,0.1352304,0.001054291,0.0002547004,0.0004904472,0.01261222,0.01462855,0.3979891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07153635,"threshold_uncertainty_score":0.2393129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02092620085274783,"score_gpt":0.2783319910912473,"score_spread":0.2574057902384995,"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."}}