{"id":"W2044746515","doi":"10.1364/fts.2011.ftud3","title":"MR-i, high speed hyperspectral imaging spectroradiometer","year":2011,"lang":"en","type":"article","venue":"Imaging and Applied Optics","topic":"Calibration and Measurement Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"ABB (Canada)","funders":"","keywords":"Hyperspectral imaging; Spectroradiometer; Spectral signature; Modular design; Spectral imaging; Product line; Full spectral imaging; Image resolution; Remote sensing; Computer science; Moderate-resolution imaging spectroradiometer; Optics; Artificial intelligence; Computer vision; Physics; Reflectivity; Astronomy; Geology; Satellite; Engineering","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":[0.00008769324,0.0001498957,0.0001313624,0.0001132832,0.00004874478,0.00007230549,0.00007869314,0.00001796672,0.00005409838],"category_scores_gemma":[0.000002851816,0.0001532687,0.00002866895,0.0001033212,0.00005432792,0.0001081323,0.00001933127,0.0001312245,0.0000152654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003249623,"about_ca_system_score_gemma":0.000005737619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009493066,"about_ca_topic_score_gemma":8.12829e-7,"domain_scores_codex":[0.999344,0.000004032033,0.0001432942,0.0001571052,0.0001088754,0.0002426774],"domain_scores_gemma":[0.9997233,0.00000740097,0.0000175212,0.0001538893,0.00001605807,0.00008185573],"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":[0.00003106136,0.00009430696,0.003645229,0.00009850506,0.00008539621,0.00004323343,0.002074125,0.000140531,0.8390785,0.1134075,0.007285523,0.03401608],"study_design_scores_gemma":[0.001258545,0.00002161379,0.00524442,0.00004677344,0.0001080301,0.0001033183,0.0008142081,0.04401104,0.9307169,0.01154885,0.005051141,0.001075179],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5249718,0.001050539,0.08103927,0.0004529784,0.0006988766,0.0005255486,0.00001420307,0.004469385,0.3867773],"genre_scores_gemma":[0.9599525,0.00006964826,0.03960368,0.000168247,0.0001124749,0.000005596088,0.000004858746,0.0000369888,0.00004599703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4349806,"threshold_uncertainty_score":0.6250117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673775719678836,"score_gpt":0.2010074073872234,"score_spread":0.1842696501904351,"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."}}