{"id":"W2886056129","doi":"10.1109/jstars.2018.2830178","title":"Corrections to “Image Classification Using RapidEye Data: Integration of Spectral and Textual Features in a Random Forest Classifier” [Dec 17 5334-5349]","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Random forest; Classifier (UML); Computer science; Pattern recognition (psychology); Artificial intelligence; Remote sensing; Contextual image classification; Geography; Image (mathematics)","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":[0.0005422718,0.0001854769,0.000363191,0.0006112062,0.0001154411,0.0001052125,0.0001122467,0.000157537,0.00000115692],"category_scores_gemma":[0.0003393352,0.000182836,0.00002759785,0.001090868,0.0001408343,0.0003053543,0.00002299619,0.0004372081,3.239598e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001282149,"about_ca_system_score_gemma":0.0001353068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001461006,"about_ca_topic_score_gemma":0.005477426,"domain_scores_codex":[0.9984493,0.00006783431,0.0007857971,0.0002400837,0.000231347,0.0002256672],"domain_scores_gemma":[0.9987357,0.0001580305,0.0002638797,0.0002865891,0.0004743925,0.0000813911],"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":[0.0001255837,0.00002682056,0.0005963076,0.0000411461,0.00003491331,0.000005779077,0.000931694,0.005988597,0.875429,0.0001401024,0.0003353604,0.1163447],"study_design_scores_gemma":[0.00112318,0.00007743188,0.1878152,0.0003455057,0.00004588618,0.0001040637,0.0005726583,0.790631,0.01821699,0.0003652879,0.0004963191,0.0002064439],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7494931,0.00006130231,0.2491767,0.0002269318,0.000306413,0.0002643513,0.000007833502,0.00002486547,0.0004384861],"genre_scores_gemma":[0.8016914,0.0001446182,0.1977133,0.00002761675,0.0003502409,9.84351e-8,0.00001825062,0.00002318578,0.00003124613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.857212,"threshold_uncertainty_score":0.7455834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05381738447492498,"score_gpt":0.2778071603788449,"score_spread":0.2239897759039199,"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."}}