{"id":"W2566833016","doi":"10.1093/jmicro/dfx129","title":"Convergent-beam EMCD: benefits, pitfalls and applications","year":2018,"lang":"en","type":"article","venue":"Microscopy","topic":"Magnetic properties of thin films","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Austrian Science Fund","keywords":"Convergence (economics); Beam (structure); Detector; Signal-to-noise ratio (imaging); Computer science; Energy (signal processing); SIGNAL (programming language); Materials science; Noise (video); Optics; Physics; Telecommunications; Artificial intelligence; Quantum mechanics","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":[0.003529813,0.0005823264,0.0006477661,0.001052104,0.0005456052,0.001607918,0.001576,0.00139131,0.001841828],"category_scores_gemma":[0.002805575,0.0005534854,0.0002849781,0.0009280219,0.001747523,0.001898518,0.00122589,0.001416384,0.0006120793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007702432,"about_ca_system_score_gemma":0.0003966548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003862992,"about_ca_topic_score_gemma":0.0006061866,"domain_scores_codex":[0.9989912,0.0003059123,0.00003694093,0.0001340037,0.0004675156,0.00006448341],"domain_scores_gemma":[0.9982744,0.0008730164,0.000107473,0.0002958105,0.0003625324,0.00008681846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008325697,0.0002440073,0.006331812,0.002290544,0.0001019776,0.001654678,0.0008782591,0.007168248,0.4246545,0.1909176,0.004893885,0.3600318],"study_design_scores_gemma":[0.00008564106,0.0005901146,0.004753501,0.0009459318,0.0001342202,0.007525539,0.0007729956,0.06207973,0.717873,0.1136083,0.09142967,0.0002013511],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1614935,0.3603943,0.418156,0.01231708,0.0005754206,0.0001266817,0.0001543537,0.001204989,0.04557762],"genre_scores_gemma":[0.5308202,0.1306414,0.3307734,0.001351938,0.0004569529,0.0001086903,0.0001078602,0.0003261914,0.005413329],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003529813,"threshold_uncertainty_score":0.0186677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007466183060174962,"score_gpt":0.2361857977622013,"score_spread":0.2287196147020263,"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."}}