{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006417607,0.0006960509,0.0005949793,0.001296414,0.0005560984,0.0007977514,0.0008867529,0.0005767882,0.01010611],"category_scores_gemma":[0.0006811406,0.0003550492,0.0002429593,0.0009775031,0.0003184765,0.001086841,0.000530853,0.0008201165,0.005765541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00042448,"about_ca_system_score_gemma":0.0004887543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001138238,"about_ca_topic_score_gemma":0.001831801,"domain_scores_codex":[0.9988285,0.0000979866,0.00002861107,0.0002878032,0.0006864555,0.00007071482],"domain_scores_gemma":[0.9994618,0.00005484563,0.00006669122,0.00008988538,0.0002748537,0.00005181436],"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":[0.0007950658,0.0002243992,0.007310996,0.0005538245,0.00009766991,0.0001754051,0.0001552758,0.003998301,0.753957,0.007393755,0.06420933,0.1611289],"study_design_scores_gemma":[0.0001541227,0.0006526771,0.01946417,0.00006527216,0.0001290811,0.001508775,0.0001459424,0.07009092,0.6146806,0.002153807,0.2907441,0.0002105758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1797982,0.00298155,0.5736465,0.001014989,0.0008032125,0.001293291,0.02974766,0.08506813,0.1256464],"genre_scores_gemma":[0.3188125,0.001137022,0.5962539,0.001055757,0.0002169061,0.0005339613,0.01961354,0.001771496,0.06060493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01010611,"threshold_uncertainty_score":0.03380835,"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."}}