{"id":"W3165739532","doi":"10.3390/s21113738","title":"Calibration of a Hyper-Spectral Imaging System Using a Low-Cost Reference","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Hyperspectral imaging; Calibration; Remote sensing; Full spectral imaging; Spectral imaging; Asphalt; Environmental science; Spectral power distribution; Radiant intensity; Principal component analysis; Optics; Spectral sensitivity; Materials science; Geology; Computer science; Artificial intelligence; Mathematics; Physics; Wavelength","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":[0.001627848,0.0006449334,0.0005851954,0.001086445,0.0005054084,0.0008400644,0.001732039,0.001476312,0.002945727],"category_scores_gemma":[0.002182554,0.0003559364,0.0003259298,0.0007448125,0.0005927001,0.001380152,0.0008632734,0.0007828556,0.002720022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005103966,"about_ca_system_score_gemma":0.0006312684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006308724,"about_ca_topic_score_gemma":0.0009444319,"domain_scores_codex":[0.9983822,0.0002418319,0.0000597354,0.0004516773,0.0007958168,0.00006869839],"domain_scores_gemma":[0.9987664,0.0002635693,0.0001227349,0.0003236206,0.0004747294,0.00004898421],"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":[0.0002375207,0.0001556734,0.003307391,0.0002666074,0.00003644726,0.0001532189,0.0001400791,0.00300336,0.8357028,0.001784321,0.001465409,0.1537471],"study_design_scores_gemma":[0.00004545373,0.0007908963,0.009725047,0.00005786958,0.0000940258,0.001328672,0.0001045046,0.05330282,0.9140583,0.0005970055,0.01979377,0.0001015993],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09884186,0.00063711,0.89288,0.0002555058,0.0001971235,0.0003004567,0.0001608372,0.00321675,0.003510422],"genre_scores_gemma":[0.3452871,0.0004924315,0.6462556,0.0004420113,0.00009015675,0.0003378475,0.0005067851,0.0003160767,0.006271924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002945727,"threshold_uncertainty_score":0.009854436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0252940560242713,"score_gpt":0.2338016477635965,"score_spread":0.2085075917393252,"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."}}