{"id":"W2131264332","doi":"10.1107/s2052252514021368","title":"X-ray techniques for innovation in industry","year":2014,"lang":"en","type":"review","venue":"IUCrJ","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada)","funders":"National Research Council Canada; Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; FP7 Research Potential of Convergence Regions; University of Saskatchewan; Canadian Light Source","keywords":"Variety (cybernetics); European commission; Characterization (materials science); Scale (ratio); Computer science; Business; Engineering management; Data science; Nanotechnology; Engineering; European union; Physics; Materials science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001548505,0.001375772,0.001344047,0.003484164,0.0009067134,0.00288767,0.001861893,0.003079406,0.02279771],"category_scores_gemma":[0.001810243,0.0005924045,0.0009503515,0.004141126,0.002180151,0.004276458,0.00268293,0.00518077,0.01459266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001465333,"about_ca_system_score_gemma":0.001997491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008418919,"about_ca_topic_score_gemma":0.0009704132,"domain_scores_codex":[0.9987055,0.0002230306,0.0001422779,0.0002156027,0.0005934983,0.0001201422],"domain_scores_gemma":[0.9990249,0.000418867,0.0001060581,0.0001344941,0.0002394948,0.00007611538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005900925,0.00009941637,0.0001809003,0.01483032,0.00007735912,0.0003496017,0.0003471137,0.0004325515,0.005046239,0.1097175,0.09144355,0.7774165],"study_design_scores_gemma":[0.000003875607,0.0000202177,0.00009286406,0.001013377,0.000008967778,0.0002893663,0.0000383726,0.00003392726,0.0005067496,0.006623556,0.9913605,0.000008071297],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000269731,0.9663203,0.002882845,0.001875565,0.002693118,0.0000530631,0.00006242566,0.00007661927,0.02576635],"genre_scores_gemma":[0.004042459,0.9702242,0.004531831,0.001514064,0.001369241,0.0001193299,0.0001447649,0.00003283075,0.01802116],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02279771,"threshold_uncertainty_score":0.07626593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05203177813916417,"score_gpt":0.3971481975444083,"score_spread":0.3451164194052442,"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."}}