{"id":"W3013803529","doi":"10.1109/tgrs.2020.2969577","title":"Estimation of Mineral Abundance From Hyperspectral Data Using a New Supervised Neighbor-Band Ratio Unmixing Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Endmember; Hyperspectral imaging; Cuprite; Robustness (evolution); Remote sensing; Atmospheric radiative transfer codes; Environmental science; Radiative transfer; Pattern recognition (psychology); Computer science; Artificial intelligence; Geology; Chemistry; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001118172,0.0001951527,0.0002355845,0.0001090534,0.000191188,0.00012017,0.0001727746,0.00008546089,0.000002718715],"category_scores_gemma":[0.00002594222,0.0002000728,0.00004232866,0.0005194921,0.0001503902,0.0006090202,0.000002889594,0.0002288412,0.00000248376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005706438,"about_ca_system_score_gemma":0.00008185627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00071887,"about_ca_topic_score_gemma":0.00002991948,"domain_scores_codex":[0.9986912,0.00003680178,0.0003089519,0.0004703534,0.0002554947,0.0002372176],"domain_scores_gemma":[0.9992668,0.00006681796,0.00006469581,0.0004112565,0.00003996725,0.0001504638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000152711,0.000007782144,3.885886e-7,0.00002950031,0.00001347322,0.000002394449,0.001086425,0.2146804,0.4783391,0.000001073175,0.00001081568,0.3058134],"study_design_scores_gemma":[0.0002912044,0.00002245437,0.00008367902,0.0000853345,0.00004832622,0.000021677,0.0002295472,0.8938826,0.1051164,0.00002612485,0.00001223189,0.0001804514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2672091,0.00006310582,0.7319902,0.0001920525,0.0002087496,0.0001220858,0.00001981413,0.0001111103,0.00008381649],"genre_scores_gemma":[0.5814137,0.00003118353,0.4184062,0.00004344789,0.0000642246,1.371128e-8,0.00001129954,0.0000186143,0.00001123268],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6792021,"threshold_uncertainty_score":0.8158732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05925417055106901,"score_gpt":0.2547149451345797,"score_spread":0.1954607745835107,"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."}}