{"id":"W4290098943","doi":"10.1002/essoar.10512069.1","title":"Using crystal lattice distortion data for geological investigations: the Weighted Burgers Vector method","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Oxford Instruments (Canada)","funders":"","keywords":"Burgers vector; Electron backscatter diffraction; Dislocation; Orientation (vector space); Distortion (music); Lattice (music); Geometry; Diffraction; Materials science; Geology; Physics; Optics; Mathematics; Condensed matter physics; Acoustics","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":[0.003361458,0.001363634,0.001155481,0.006910903,0.0005499827,0.002445933,0.001743658,0.0007557429,0.006207522],"category_scores_gemma":[0.008506343,0.0007308481,0.000833615,0.005933738,0.0006712529,0.00272049,0.001551721,0.001688415,0.002789161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006407492,"about_ca_system_score_gemma":0.001939294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003365427,"about_ca_topic_score_gemma":0.00505413,"domain_scores_codex":[0.997445,0.0006103626,0.0002315202,0.0003637977,0.001251565,0.00009774344],"domain_scores_gemma":[0.9963673,0.001537842,0.0005299014,0.0005013588,0.0009522488,0.0001114126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002108006,0.00008607993,0.0101205,0.0009370078,0.00022365,0.0002082726,0.0004059446,0.04290581,0.03025836,0.05451424,0.01188223,0.8482472],"study_design_scores_gemma":[0.0001030357,0.0001798139,0.01148917,0.0002610368,0.0001118792,0.001052347,0.0005372969,0.7600439,0.03521739,0.09582173,0.09478356,0.0003988753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004660382,0.0004327088,0.9911466,0.00007390272,0.0000768126,0.0001080981,0.0008890822,0.00143869,0.001173679],"genre_scores_gemma":[0.0390464,0.0007301844,0.9563448,0.0000396613,0.00003834518,0.0002350156,0.001714988,0.0006352785,0.00121525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006910903,"threshold_uncertainty_score":0.02076626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1614360524386348,"score_gpt":0.3399096602200514,"score_spread":0.1784736077814166,"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."}}