{"id":"W2560029930","doi":"10.1017/s1431927615010582","title":"Spin-Multislice Applied to the Electron Spin Interaction with Materials","year":2015,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Magnetic properties of thin films","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Spin (aerodynamics); Multislice; Condensed matter physics; Materials science; Electron; Physics; Nuclear magnetic resonance; 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":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003207413,0.0005031864,0.0005750177,0.0006141075,0.0003187803,0.0005602601,0.0004654333,0.0005060445,0.003068057],"category_scores_gemma":[0.0005486306,0.0003761205,0.0002768829,0.0002917528,0.0005575428,0.0005136523,0.0008570302,0.001094126,0.0003618007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004684212,"about_ca_system_score_gemma":0.0002072292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003395584,"about_ca_topic_score_gemma":0.0009303364,"domain_scores_codex":[0.9998432,0.00004897473,0.000005664383,0.00001709529,0.00006545366,0.00001956813],"domain_scores_gemma":[0.9997777,0.00009493004,0.00001839917,0.00004781885,0.00003699174,0.0000242502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004003706,0.000163302,0.0004332255,0.0004852083,0.00007523515,0.0004563415,0.0003103654,0.01536506,0.7282577,0.1456365,0.005135526,0.1032812],"study_design_scores_gemma":[0.00008007562,0.0001809352,0.001282564,0.00008655233,0.00003196608,0.0004678509,0.00006338231,0.5479134,0.4048614,0.02470267,0.02025292,0.00007635151],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2285389,0.01128465,0.716148,0.001164505,0.00109815,0.0001449406,0.0001279358,0.004243104,0.03724978],"genre_scores_gemma":[0.8229982,0.002734991,0.1647162,0.0001998594,0.0001786171,0.0001227899,0.00008245827,0.0006211895,0.008345509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003068057,"threshold_uncertainty_score":0.01026368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009715892629987538,"score_gpt":0.2603573360803147,"score_spread":0.2506414434503272,"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."}}