{"id":"W3095966380","doi":"10.1063/5.0035395","title":"Learning-based approach to plasticity in athermal sheared amorphous packings: Improving softness","year":2021,"lang":"en","type":"preprint","venue":"APL Materials","topic":"Topological and Geometric Data Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Simons Foundation; U.S. Department of Energy","keywords":"Plasticity; Persistent homology; Statistical physics; Representation (politics); SPHERES; Computer science; Hard spheres; Particle (ecology); Simple (philosophy); Biological system; Artificial intelligence; Algorithm; Physics; Thermodynamics; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001625945,0.0008150048,0.001440834,0.002472824,0.0006394141,0.001361961,0.002054473,0.001299432,0.0010873],"category_scores_gemma":[0.006457604,0.0005198061,0.0009741176,0.001547732,0.001595487,0.00244387,0.001957409,0.001834044,0.0004819296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009990452,"about_ca_system_score_gemma":0.0008175155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00367799,"about_ca_topic_score_gemma":0.00378428,"domain_scores_codex":[0.9993458,0.0001569631,0.00005629232,0.0001960176,0.0001759371,0.00006883575],"domain_scores_gemma":[0.9961064,0.001779818,0.0006413519,0.0008312717,0.0004320266,0.0002090723],"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":[0.0001962461,0.0001817318,0.005628811,0.0001675831,0.0001212716,0.0001196728,0.0001726914,0.8467401,0.01095018,0.01391727,0.001050536,0.1207539],"study_design_scores_gemma":[0.00000336689,0.00001368986,0.0002297961,0.000003543267,0.000003357883,0.000007766223,0.000006029238,0.9948791,0.0008096864,0.003945857,0.00009313972,0.000004547299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2145431,0.0005665115,0.78122,0.0004936258,0.00003967496,0.00006065144,0.000282124,0.001573118,0.001221288],"genre_scores_gemma":[0.8035701,0.0003827313,0.1926314,0.0002799748,0.0001295368,0.0001177363,0.0008333899,0.000252616,0.001802519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00367799,"threshold_uncertainty_score":0.008598924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0187060652621013,"score_gpt":0.2349550200398586,"score_spread":0.2162489547777573,"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."}}