{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003374891,0.0002170869,0.0003822875,0.0004089245,0.0006121151,0.001733907,0.0004002152,0.0007487708,0.001884335],"category_scores_gemma":[0.0009444557,0.0001741261,0.0002844043,0.0003142036,0.002807638,0.002616018,0.002061799,0.0009828344,0.0003013205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002718421,"about_ca_system_score_gemma":0.0002613179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000519194,"about_ca_topic_score_gemma":0.0005497272,"domain_scores_codex":[0.9998367,0.0000249279,0.00001041278,0.00004200528,0.00004839936,0.00003762992],"domain_scores_gemma":[0.9996566,0.00008637356,0.00006564377,0.00005697761,0.00003508448,0.00009938767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002523019,0.00006735192,0.005860211,0.0004672032,0.00004601033,0.001169568,0.001272705,0.02445039,0.04941981,0.8389888,0.004731308,0.07327428],"study_design_scores_gemma":[0.00003334467,0.0002909459,0.01177939,0.0002705449,0.00003379974,0.001247247,0.0009687138,0.1006406,0.009550451,0.8267033,0.04838026,0.0001014293],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.643685,0.01740227,0.2623773,0.006197473,0.001160528,0.000114986,0.0003184943,0.00040737,0.06833644],"genre_scores_gemma":[0.965322,0.003158111,0.02268587,0.0007616313,0.0003019804,0.00002398512,0.00008427294,0.00005898401,0.007603093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001884335,"threshold_uncertainty_score":0.006303728,"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."}}