{"id":"W2921302801","doi":"10.1017/s1551929518001256","title":"Correlative Spectromicroscopy and Tomography for Biomedical Applications Involving Electron, Ion, and Soft X-ray Microscopies","year":2019,"lang":"en","type":"article","venue":"Microscopy Today","topic":"Advanced Materials Characterization Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Research Council Canada; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Göteborgs Universitet; McMaster University","keywords":"Correlative; Tomography; Electron; Materials science; Ion; Nanotechnology; X-ray; Chemistry; Physics; Optics; Nuclear physics; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001253909,0.0002725256,0.0003330956,0.0001852798,0.0001331625,0.000118298,0.0001268182,0.0001608262,0.00003659254],"category_scores_gemma":[0.00001499912,0.0002856485,0.00003992746,0.0001961773,0.0001797682,0.0002584275,0.0000567591,0.0001459563,0.00001432834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006354472,"about_ca_system_score_gemma":0.00002164839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004069544,"about_ca_topic_score_gemma":0.000001867679,"domain_scores_codex":[0.9988035,0.00001712683,0.0003301154,0.0003886445,0.00007653612,0.0003840553],"domain_scores_gemma":[0.999427,0.000103781,0.00008103735,0.0002300518,0.0000579591,0.0001001593],"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":[0.00002153457,0.00001972754,0.001167405,0.0002264823,0.00002599586,1.830864e-7,0.0002793843,0.000009106191,0.9970117,0.0005594668,0.000475603,0.0002034081],"study_design_scores_gemma":[0.0004928079,0.0001637097,0.00101669,0.0000921736,0.00002092048,0.000006652595,0.00005592249,0.0002965969,0.962778,0.001719976,0.03301121,0.0003453933],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6747386,0.002026737,0.3206284,0.00007620917,0.0003109401,0.001384486,0.0001759926,0.0006073358,0.00005120546],"genre_scores_gemma":[0.8793492,0.001627446,0.1168971,0.0002595714,0.0002244669,0.0007513819,0.0003682907,0.0001825092,0.0003400357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2046105,"threshold_uncertainty_score":0.9999596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002961347849565718,"score_gpt":0.2288301004586882,"score_spread":0.2258687526091225,"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."}}