{"id":"W4413770817","doi":"10.1016/j.rsase.2025.101700","title":"Deep hyperspectral clustering using attention-enhanced 3D-2D convolutional autoencoder for mineral mapping","year":2025,"lang":"en","type":"article","venue":"Remote Sensing Applications Society and Environment","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Canadian Space Agency","keywords":"Autoencoder; Hyperspectral imaging; Cluster analysis; Artificial intelligence; Computer science; Pattern recognition (psychology); Convolutional neural network; Environmental science; Geology; Deep learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002539839,0.0007059386,0.0006359733,0.0006086851,0.0003389646,0.0004202302,0.001005075,0.0006781627,0.001831953],"category_scores_gemma":[0.0004195989,0.0003399615,0.0008397627,0.0006534265,0.000279905,0.0005720714,0.0008596661,0.0008508064,0.0007823936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005468579,"about_ca_system_score_gemma":0.001071182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02376493,"about_ca_topic_score_gemma":0.03974834,"domain_scores_codex":[0.9998387,0.00001919501,0.000007001969,0.00005106744,0.00004251774,0.00004137902],"domain_scores_gemma":[0.9998266,0.00003620558,0.00001704239,0.00003320605,0.00007161885,0.00001531898],"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":[0.0002532763,0.0002754744,0.00244179,0.00009165733,0.0001695818,0.00008922468,0.00009441446,0.3216411,0.05047263,0.00334597,0.00940476,0.6117201],"study_design_scores_gemma":[0.000002783435,0.000009411559,0.0004225885,0.000002327797,0.000008980865,0.000009307858,0.000007795898,0.9951143,0.003321328,0.0006973318,0.0003995872,0.000004141594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1256667,0.001103669,0.8650245,0.0004746658,0.0001650519,0.00004948329,0.0006174152,0.003521445,0.003377125],"genre_scores_gemma":[0.7442199,0.0005711142,0.2408918,0.0003499802,0.0001028031,0.00007014525,0.002281322,0.0002107751,0.01130209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02376493,"threshold_uncertainty_score":0.04725325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486716703218391,"score_gpt":0.2272100200936831,"score_spread":0.2123428530614992,"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."}}