{"id":"W2057808716","doi":"10.1103/physrevlett.88.245701","title":"Modeling Elasticity in Crystal Growth","year":2002,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Microstructure and mechanical properties","field":"Materials Science","cited_by":961,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Epitaxy; Materials science; Elasticity (physics); Crystal growth; Grain boundary; Condensed matter physics; Statistical physics; Crystal (programming language); Growth model; Physics; Nanotechnology; Thermodynamics; Computer science; Microstructure; Mathematics; Composite material","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":[0.0002646084,0.0004045719,0.0005143314,0.0003873454,0.0006552716,0.0009166092,0.00110378,0.001493987,0.002085986],"category_scores_gemma":[0.001085198,0.0004855962,0.0005791594,0.0003880979,0.0007918797,0.001467077,0.0009354023,0.0008076028,0.000332538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009886628,"about_ca_system_score_gemma":0.0008666649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008390397,"about_ca_topic_score_gemma":0.00447045,"domain_scores_codex":[0.999897,0.00002407877,0.000004911138,0.00002228328,0.00002887937,0.00002290235],"domain_scores_gemma":[0.9997376,0.0001335958,0.00003493913,0.00002865377,0.00003125321,0.0000340344],"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":[0.00001274927,0.00001550358,0.0003763951,0.00001887616,0.000007327396,0.00006721551,0.00003106217,0.9512469,0.002115663,0.04371206,0.0002549606,0.002141176],"study_design_scores_gemma":[0.000005378513,0.000004386814,0.00007600622,0.000002453887,0.000001782392,0.00001470671,0.000005784871,0.9879012,0.0001698368,0.0109926,0.000820832,0.000005138575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3278688,0.002257721,0.6154709,0.002755665,0.0004076793,0.0001103577,0.0006660559,0.0005235731,0.04993919],"genre_scores_gemma":[0.9185995,0.001802569,0.06060363,0.0002347028,0.0001085546,0.0002624261,0.0002843218,0.0001633481,0.01794112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008390397,"threshold_uncertainty_score":0.0166831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02999098228093728,"score_gpt":0.2534757913145189,"score_spread":0.2234848090335816,"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."}}