{"id":"W2227496933","doi":"10.1039/c5fd00176e","title":"High-throughput quantum cascade laser (QCL) spectral histopathology: a practical approach towards clinical translation","year":2016,"lang":"en","type":"article","venue":"Faraday Discussions","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Chemical imaging; Pixel; Throughput; Data acquisition; Digital pathology; Quantum cascade laser; Artificial intelligence; Data science; Medical physics; Nanotechnology; Materials science; Laser; Hyperspectral imaging; Optics; Medicine; Physics; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0005756421,0.0002420934,0.0003062049,0.00005229983,0.0001345339,0.00001903951,0.0003400072,0.0005651355,0.0002549673],"category_scores_gemma":[0.0007178102,0.0001257577,0.000264402,0.0001541947,0.0007140775,0.00001590534,0.0001898487,0.0004167772,0.00004680071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006125015,"about_ca_system_score_gemma":0.0002123925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000180906,"about_ca_topic_score_gemma":0.000009087777,"domain_scores_codex":[0.9974962,0.0002631219,0.0004909734,0.0007521249,0.0004139189,0.0005836921],"domain_scores_gemma":[0.9988086,0.00009233828,0.00008789475,0.0005820527,0.00005489726,0.0003742248],"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":[0.0005284824,0.00118514,0.0007602282,0.00002366477,0.0000618731,0.00005133404,0.0000408433,3.684681e-7,0.8688103,0.007867281,0.08920665,0.03146384],"study_design_scores_gemma":[0.001928589,0.001187665,0.005162083,0.00005621986,0.00007155785,0.0001547052,0.00006175683,0.0001024487,0.3676387,0.008511503,0.6144963,0.0006284478],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.549839,0.0005188641,0.3922165,0.04346343,0.001009136,0.0008498798,0.0003437579,0.0002702868,0.01148917],"genre_scores_gemma":[0.9550653,0.0007583472,0.03836033,0.0004758689,0.001018874,0.0001118615,0.0002082788,0.00004530485,0.003955908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5252897,"threshold_uncertainty_score":0.5128248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04937942772323209,"score_gpt":0.3958973624785511,"score_spread":0.346517934755319,"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."}}