{"id":"W4414957828","doi":"10.1073/pnas.2509578122","title":"Predicting forced responses of probability distributions via the fluctuation–dissipation theorem and generative modeling","year":2025,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Nonlinear system; Gaussian; Matching (statistics); Function (biology); Score; Probability distribution; Generative model; Feature (linguistics); Probability density function","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001145386,0.000579683,0.0005332395,0.0006435169,0.000318488,0.0007849737,0.001134695,0.0007585309,0.0006755389],"category_scores_gemma":[0.004713499,0.000456914,0.0006969496,0.0004215126,0.001006208,0.0009538084,0.0009898961,0.0008750953,0.0001504966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007821599,"about_ca_system_score_gemma":0.0008099488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005339031,"about_ca_topic_score_gemma":0.004972552,"domain_scores_codex":[0.999723,0.00009566973,0.00001414443,0.00005894659,0.00007962326,0.00002859952],"domain_scores_gemma":[0.9986268,0.0008727564,0.0001802104,0.000142456,0.0001093985,0.00006834753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009873988,0.00001164407,0.000733704,0.00001224493,0.00001532024,0.00002567353,0.00002137736,0.9827306,0.001273245,0.009000188,0.000128081,0.006038134],"study_design_scores_gemma":[4.970655e-7,0.000001214516,0.00006031334,5.008436e-7,5.101621e-7,0.000001681067,6.093096e-7,0.9975535,0.0001321281,0.002222328,0.00002506929,0.000001687894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0363007,0.00005002094,0.962709,0.0001190513,0.00001412538,0.00001457368,0.00005246756,0.0002354164,0.0005046201],"genre_scores_gemma":[0.8966976,0.0001099369,0.1016119,0.00006718932,0.00003111186,0.00007957296,0.000169609,0.0001113673,0.00112172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005339031,"threshold_uncertainty_score":0.01061589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04275874488864559,"score_gpt":0.315315804302342,"score_spread":0.2725570594136963,"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."}}