{"id":"W1600751823","doi":"10.7773/cm.v34i4.1320","title":"Mapping the condition of mangroves of the Mexican Pacific using C-band ENVISAT ASAR and Landsat optical data","year":2008,"lang":"en","type":"article","venue":"Ciencias Marinas","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University","funders":"","keywords":"Mangrove; Remote sensing; Geography; Environmental science; Confusion; Forestry; Fishery","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":[0.0001507366,0.00006949382,0.000100982,0.00003903659,0.0001259866,0.00000975508,0.0001921197,0.00004407474,0.000008370768],"category_scores_gemma":[0.00003976801,0.0000430427,0.00002215538,0.0001546011,0.0001971031,0.0001323371,0.00008481208,0.00009153511,5.390004e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001776653,"about_ca_system_score_gemma":0.00001348232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002767821,"about_ca_topic_score_gemma":0.000002852198,"domain_scores_codex":[0.999495,0.00001706068,0.0001414425,0.0001060746,0.0001337768,0.0001066242],"domain_scores_gemma":[0.9995598,0.00005685831,0.00005211308,0.0002974424,0.00001462828,0.00001918804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002830638,0.0001418798,0.1121619,0.000514431,0.0001790802,0.00002172105,0.005266865,0.01624314,0.836446,0.001570455,0.002391589,0.02503461],"study_design_scores_gemma":[0.0003823605,0.00003841813,0.7619993,0.0001414303,0.000058875,0.0002631132,0.001457599,0.1538246,0.07854569,0.000358148,0.002696914,0.0002335099],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964047,0.000157489,0.0008658683,0.00003046913,0.0001377361,0.0000771905,0.00001218176,0.00003534974,0.002279025],"genre_scores_gemma":[0.9996102,0.00007115358,0.0002320006,0.000006568102,0.00003454025,8.206795e-7,0.000006849195,0.000007528616,0.00003031618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7579003,"threshold_uncertainty_score":0.175523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05520013969798711,"score_gpt":0.2366642965923244,"score_spread":0.1814641568943373,"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."}}