{"id":"W4281619778","doi":"10.4279/pip.140009","title":"Softer than soft: Diving into squishy granular matter","year":2022,"lang":"en","type":"article","venue":"Papers in Physics","topic":"Granular flow and fluidized beds","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Soft matter; Rheology; Granular material; Distortion (music); Deformation (meteorology); Tearing; Granular matter; Field (mathematics); Mechanics; Physics; Statistical physics; Classical mechanics; Computer science; Geotechnical engineering; Geology; Mechanical engineering; Engineering; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001033068,0.0001676154,0.0001735859,0.00005335137,0.0001248226,0.00002772865,0.0002147526,0.00003472102,0.0004080032],"category_scores_gemma":[0.000003497195,0.0001876033,0.0001017025,0.0002505173,0.00002721662,0.00008136735,0.0001257782,0.0003196406,0.0001015865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001003136,"about_ca_system_score_gemma":0.000007656904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002139066,"about_ca_topic_score_gemma":0.000007781975,"domain_scores_codex":[0.9990934,0.00003345356,0.000162371,0.0001860993,0.0002226819,0.0003019855],"domain_scores_gemma":[0.9996475,0.00003013036,0.00001381524,0.0002567066,0.000006151653,0.00004569584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008184444,0.0005034519,0.3181349,0.0008009195,0.0002551213,0.0002309667,0.02444266,0.08300754,0.3012371,0.003378359,0.01060876,0.2573184],"study_design_scores_gemma":[0.009938978,0.0004269627,0.06614351,0.0003710521,0.000387558,0.00009436192,0.004005571,0.236366,0.05013698,0.09778409,0.5263756,0.00796934],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9070849,0.001299607,0.008282998,0.0002894458,0.002556053,0.000492881,0.00003886686,0.0007538232,0.07920138],"genre_scores_gemma":[0.9988207,0.00001796024,0.0003763966,0.0003769735,0.000161246,0.00008676644,0.00002383087,0.00006087394,0.00007530609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5157669,"threshold_uncertainty_score":0.7650239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004260585518701838,"score_gpt":0.1823139317453781,"score_spread":0.1780533462266762,"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."}}