{"id":"W4385763559","doi":"10.1080/07038992.2023.2237591","title":"Black and Odorous Water Detection of Remote Sensing Images Based on Improved Deep Learning","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Remote sensing; Computer science; Satellite; Segmentation; Feature (linguistics); Environmental science; Deep learning; Geography; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007942963,0.0001535799,0.0002321035,0.0003577441,0.0002169834,0.00006596911,0.0001114089,0.0001198571,0.000006010284],"category_scores_gemma":[0.0004121125,0.0001310751,0.0000691945,0.0002715645,0.0003097303,0.0001277814,0.0000484769,0.0004201094,0.00002213416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003425175,"about_ca_system_score_gemma":0.00003408615,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01850825,"about_ca_topic_score_gemma":0.005422607,"domain_scores_codex":[0.9986838,0.0001218937,0.0003439225,0.0002009315,0.0002178836,0.0004316345],"domain_scores_gemma":[0.9992839,0.00007746818,0.0002013976,0.0002037438,0.00004508157,0.0001883979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000154363,5.667565e-7,0.0001459149,0.00001382151,0.000008437111,0.0002018129,0.000644554,0.003304451,0.2883056,3.957086e-8,0.00001214435,0.7073472],"study_design_scores_gemma":[0.0003280384,0.0002221383,0.005531302,0.0002123817,0.00002818321,0.0002170815,0.0009076125,0.3484583,0.6417878,0.0004930302,0.001597331,0.0002167684],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9756386,0.00001512806,0.02302654,0.0007647564,0.0002837974,0.000070181,7.31609e-7,0.00005612693,0.0001441748],"genre_scores_gemma":[0.9523065,0.00001079549,0.04749262,0.00004071163,0.00005770587,1.491472e-9,7.829172e-7,0.0000268683,0.00006402681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7071304,"threshold_uncertainty_score":0.9880276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01671206108056996,"score_gpt":0.2222791159040859,"score_spread":0.205567054823516,"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."}}