{"id":"W4388752509","doi":"10.1088/1361-6382/ad0db1","title":"Constraining the quantum gravity polymer scale using LIGO data","year":2023,"lang":"en","type":"article","venue":"Classical and Quantum Gravity","topic":"Pulsars and Gravitational Waves Research","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Physics; Computer science","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":[0.001841625,0.0003688804,0.0003491752,0.001292529,0.0003347454,0.001533578,0.0004925426,0.0005572745,0.002970332],"category_scores_gemma":[0.007863668,0.0003967904,0.000341062,0.0009486913,0.0008011769,0.002049754,0.001180826,0.0009037051,0.0006271959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006793804,"about_ca_system_score_gemma":0.0003096331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001696276,"about_ca_topic_score_gemma":0.001610609,"domain_scores_codex":[0.9995521,0.00009220043,0.00001927355,0.0001574753,0.00009816238,0.00008072561],"domain_scores_gemma":[0.9956704,0.001842298,0.001017639,0.001012936,0.0002069702,0.000249623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001235169,0.0002245412,0.4767497,0.0004821972,0.0005278394,0.0006401302,0.0008469064,0.187538,0.06923282,0.1930266,0.0137034,0.0557926],"study_design_scores_gemma":[0.0001318416,0.0001358116,0.3767066,0.0001010592,0.0001677237,0.0002266755,0.0001682528,0.4618921,0.02306125,0.1100955,0.0270053,0.0003078351],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9419379,0.0002359569,0.03723127,0.0005110049,0.00002762228,0.00002421201,0.004553432,0.0007235479,0.01475494],"genre_scores_gemma":[0.9943685,0.00007615075,0.003022779,0.00007098938,0.00002400998,0.0000136135,0.002111895,0.0001068787,0.0002050763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002970332,"threshold_uncertainty_score":0.00993675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07865093891451927,"score_gpt":0.3781302073281199,"score_spread":0.2994792684136006,"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."}}