{"id":"W2793378668","doi":"10.1117/12.2282532","title":"Hyperspectral imaging: comparison of acousto-optic and liquid crystal tunable filters","year":2018,"lang":"en","type":"article","venue":"Medical Imaging 2018: Physics of Medical Imaging","topic":"Optical and Acousto-Optic Technologies","field":"Physics and Astronomy","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Juravinski Cancer Centre; McMaster University","funders":"","keywords":"Liquid crystal tunable filter; Hyperspectral imaging; Image resolution; Spectral resolution; Spectral imaging; Optics; Filter (signal processing); Optical filter; Image quality; Materials science; Leverage (statistics); Computer science; Wideband; Wavelength; Resolution (logic); Artificial intelligence; Computer vision; Spectral line; Optoelectronics; Image (mathematics); Physics","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":[0.0005893087,0.0003235087,0.0003148813,0.000556598,0.000204959,0.0006464226,0.0004960929,0.0006236302,0.001270189],"category_scores_gemma":[0.001154471,0.0001304658,0.0002629947,0.000452445,0.0002785096,0.001027787,0.0003622606,0.0003370251,0.0002881358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00044803,"about_ca_system_score_gemma":0.0002925455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001092628,"about_ca_topic_score_gemma":0.00140271,"domain_scores_codex":[0.9994168,0.00006353406,0.00002118534,0.0001142929,0.0003279318,0.00005629777],"domain_scores_gemma":[0.9992624,0.0003242313,0.0001195846,0.00005057899,0.0002030534,0.00004008908],"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":[0.0005736565,0.00007461028,0.001316933,0.0001541421,0.00003981333,0.00005926096,0.00004330566,0.001829334,0.9556518,0.0005883893,0.0001766973,0.03949226],"study_design_scores_gemma":[0.00001897332,0.0003943569,0.003230555,0.00001477813,0.00006044747,0.0003409855,0.00003698566,0.03596395,0.9571859,0.0001124665,0.002608932,0.00003171702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8511981,0.004018988,0.1342708,0.0002985808,0.0001304165,0.00008077785,0.0001940756,0.0007308024,0.009077587],"genre_scores_gemma":[0.8912755,0.001617326,0.1034438,0.0001553496,0.00006271945,0.00006220859,0.0002237821,0.0001128409,0.003046608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001270189,"threshold_uncertainty_score":0.004249156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01130107708645744,"score_gpt":0.2871213490167297,"score_spread":0.2758202719302723,"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."}}