{"id":"W4406998231","doi":"10.1016/j.rsase.2025.101464","title":"Analysis of asbestos-cement roof classification in urban areas: Supervised and unsupervised methods with multispectral and hyperspectral remote sensing","year":2025,"lang":"en","type":"article","venue":"Remote Sensing Applications Society and Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Public Health","funders":"European Space Agency; Universidad de Granada; Ryerson University; Universidad de Cartagena; Sistema General de Regalías de Colombia; Toronto Metropolitan University","keywords":"Multispectral image; Hyperspectral imaging; Asbestos cement; Asbestos; Remote sensing; Multispectral pattern recognition; Environmental science; Artificial intelligence; Cartography; Computer science; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004052335,0.0002781657,0.0004446131,0.0002425649,0.0001756603,0.0000557687,0.00005835147,0.0001615285,0.000001510535],"category_scores_gemma":[0.00001307843,0.0002919934,0.00009137092,0.0007417325,0.0003335111,0.00008596769,0.00004266531,0.0002373496,5.463569e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003091775,"about_ca_system_score_gemma":0.00002129873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000211427,"about_ca_topic_score_gemma":0.00005502315,"domain_scores_codex":[0.998426,0.00009847981,0.000454379,0.0005680191,0.0001751505,0.0002779857],"domain_scores_gemma":[0.9990965,0.0001660647,0.00009508532,0.0005153666,0.0000321259,0.00009489062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002060775,0.00003175978,0.0008186779,0.0001563598,0.000588546,0.000001300114,0.002023172,0.009699192,0.2146866,0.00009497289,0.00001308357,0.7718658],"study_design_scores_gemma":[0.0005517473,0.00001922773,0.05759918,0.0000852678,0.0006255879,0.000009777774,0.001390375,0.9345022,0.004389516,0.0001901269,0.000388647,0.0002483316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3982935,0.0007790775,0.5997651,0.0004599398,0.00001187453,0.0004258878,0.000004693063,0.00006938283,0.0001904823],"genre_scores_gemma":[0.5543804,0.001868184,0.4436147,0.00003793151,0.0000125914,4.31289e-7,0.0000356629,0.0000239287,0.00002621943],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.924803,"threshold_uncertainty_score":0.9999532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01478453725813527,"score_gpt":0.2530137882383608,"score_spread":0.2382292509802256,"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."}}