{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001821939,0.0001921381,0.0003214809,0.0001423891,0.00004853121,0.0001197611,0.00006849217,0.0001070103,0.00001467838],"category_scores_gemma":[0.0000542016,0.0002150856,0.00003515175,0.0001618124,0.00006191678,0.0004075237,0.00003846211,0.000136366,0.0000123731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004049375,"about_ca_system_score_gemma":0.00002140844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001865386,"about_ca_topic_score_gemma":0.000001150438,"domain_scores_codex":[0.998876,0.00006482875,0.0003666433,0.0002738337,0.0001345965,0.0002841128],"domain_scores_gemma":[0.9993051,0.00009516199,0.0001031898,0.0003787029,0.00003334028,0.00008447977],"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":[0.000008097327,0.0000023813,0.0001420898,0.0001477165,0.0000172535,0.00001125181,0.000223177,0.0006870405,0.975425,0.00007302145,0.00006503369,0.02319792],"study_design_scores_gemma":[0.00017008,0.00001724586,0.001865414,0.0002397813,0.00001625903,0.00008969514,0.00007366583,0.4083958,0.5884605,0.0002663583,0.0002074517,0.0001977772],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885334,0.0001433969,0.006556728,0.00005155285,0.0004569996,0.000177952,0.000004255139,0.0004256317,0.003650077],"genre_scores_gemma":[0.8957013,0.00002012562,0.1039199,0.00002779031,0.0001813062,2.911142e-9,0.00001222973,0.00008003759,0.0000573289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4077087,"threshold_uncertainty_score":0.8770939,"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."}}