{"id":"W2974391216","doi":"10.3390/rs11182149","title":"KLUM: An Urban VNIR and SWIR Spectral Library Consisting of Building Materials","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Deutscher Akademischer Austauschdienst; Karlsruhe Institute of Technology","keywords":"VNIR; Remote sensing; Hyperspectral imaging; Facade; Imaging spectroscopy; Environmental science; Spectral line; Computer science; Geology; Physics; Engineering; Civil 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":[0.0005911692,0.001857704,0.0009314079,0.005446996,0.000719899,0.0009635477,0.001274653,0.000812069,0.007540333],"category_scores_gemma":[0.0006657271,0.0005867886,0.00116817,0.0051652,0.0004266127,0.001486663,0.001317838,0.000624022,0.008491311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003603797,"about_ca_system_score_gemma":0.0005913115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002783187,"about_ca_topic_score_gemma":0.006889096,"domain_scores_codex":[0.9993181,0.00006462341,0.00003495339,0.0002057078,0.0002800791,0.00009663614],"domain_scores_gemma":[0.9995528,0.0000548591,0.00005290263,0.000119064,0.0001730798,0.00004724291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002304018,0.001137008,0.03399003,0.004019234,0.0006799548,0.001869884,0.001010901,0.0280018,0.2658651,0.002716874,0.1529738,0.5054314],"study_design_scores_gemma":[0.0003462622,0.0006603518,0.2667637,0.0006305508,0.0006568157,0.00293466,0.001167174,0.1057146,0.2001113,0.003673618,0.4166597,0.0006813779],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.3808561,0.003609017,0.2272604,0.0003307007,0.0003883091,0.0008874245,0.2794726,0.06672077,0.04047467],"genre_scores_gemma":[0.3358131,0.001433961,0.2519962,0.0003656143,0.0001258436,0.001184932,0.3906313,0.008211219,0.01023771],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.007540333,"threshold_uncertainty_score":0.02522498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009358529045104166,"score_gpt":0.2078127234708902,"score_spread":0.198454194425786,"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."}}