{"id":"W6892169495","doi":"10.48660/23060101","title":"Machine Learning of Conserved Quantities and Symmetry Invariants","year":2023,"lang":"en","type":"other","venue":"PIRSA","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mitacs","funders":"","keywords":"Symmetry (geometry); Sequence (biology); Feature (linguistics); Conserved quantity; Invariant (physics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003279825,0.0002564388,0.0005377337,0.0005349402,0.00003440811,0.00002629448,0.0001546314,0.0002265715,0.001889448],"category_scores_gemma":[0.0005920664,0.0002539015,0.00005767197,0.0002821003,0.0001902682,0.00003415906,0.0001529679,0.0002992461,0.002970203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002491664,"about_ca_system_score_gemma":0.00006072597,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008556094,"about_ca_topic_score_gemma":0.006591853,"domain_scores_codex":[0.9988433,0.000151618,0.0002352989,0.0002949661,0.0002453455,0.0002295146],"domain_scores_gemma":[0.9990305,0.0002604934,0.0003593227,0.000263375,0.00002559423,0.0000607657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001062335,0.0001097232,0.2002548,0.002356403,0.001197359,0.0001477996,0.0006634074,0.000005309218,0.001793405,0.02274521,0.7695208,0.001099567],"study_design_scores_gemma":[0.001755624,0.0001548002,0.01092745,0.002720992,0.0002962127,0.00002295617,0.0003759556,0.0007327384,0.0003035953,0.0004193628,0.9813917,0.0008986649],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.003975453,0.01149928,0.00001802952,0.00005394822,0.0004597137,0.0003689708,0.001315363,0.002072893,0.9802364],"genre_scores_gemma":[0.06371091,0.00145804,0.0002888596,0.00002795727,0.0001509452,0.0000120246,0.0001110935,0.003746585,0.9304936],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2118708,"threshold_uncertainty_score":0.9999913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0350989290252613,"score_gpt":0.2686241143009193,"score_spread":0.233525185275658,"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."}}