{"id":"W2209236644","doi":"10.1039/c5sc03782d","title":"Wide-field tissue polarimetry allows efficient localized mass spectrometry imaging of biological tissues","year":2015,"lang":"en","type":"article","venue":"Chemical Science","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Princess Margaret Cancer Centre; University Health Network; University of Toronto; St. Michael's Hospital; Toronto Public Health","funders":"","keywords":"Mass spectrometry imaging; Mass spectrometry; Polarimetry; Characterization (materials science); Biological tissue; Chemistry; Materials science; Nanotechnology; Physics; Optics; Chromatography; Scattering","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004960716,0.0001876708,0.0002923462,0.0001341041,0.00008130797,0.00005439833,0.00100571,0.0001230118,0.001629049],"category_scores_gemma":[0.0009788949,0.0001553488,0.00007343269,0.001341786,0.0007398502,0.00007636821,0.000312787,0.0002744664,0.00003311266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001941547,"about_ca_system_score_gemma":0.0001063755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008890527,"about_ca_topic_score_gemma":1.850627e-7,"domain_scores_codex":[0.997933,0.000009626163,0.0003598687,0.0005786499,0.0006056143,0.0005132748],"domain_scores_gemma":[0.9985972,0.0001746746,0.0001407197,0.000567252,0.000149366,0.0003707547],"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":[0.00001307343,0.0001128051,0.007569543,0.00001477034,0.000003271153,0.000004091758,0.00002439373,0.000002117619,0.9855348,0.004675679,0.0005068055,0.001538618],"study_design_scores_gemma":[0.0001920625,0.00003265527,0.00007359707,0.00002283552,0.000007926307,0.00001309029,0.0000865157,0.0007080899,0.9875808,0.004572432,0.006508858,0.0002011478],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8539802,0.0005804177,0.02609419,0.0009718995,0.00006902015,0.000149487,0.00001926555,0.0003478404,0.1177877],"genre_scores_gemma":[0.9550522,0.00001137239,0.04456948,0.0001145813,0.00007356358,0.000009141702,0.000007217693,0.0000110982,0.0001513359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1176364,"threshold_uncertainty_score":0.9992836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02027487515713693,"score_gpt":0.2997419900437148,"score_spread":0.2794671148865778,"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."}}