{"id":"W2117427131","doi":"10.1590/s0001-37652003000300007","title":"Use of synthetic aperture radar for recognition of Coastal Geomorphological Features, land-use assessment and shoreline changes in Bragança coast, Pará, Northern Brazil","year":2003,"lang":"en","type":"article","venue":"Anais da Academia Brasileira de Ciências","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency; Universidade Federal do Pará; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Mangrove; Shore; Synthetic aperture radar; Geology; Coastal erosion; Remote sensing; Salt marsh; Land cover; Marsh; Land use; Oceanography; Physical geography; Geography; Wetland; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002821546,0.0002749801,0.0001704011,0.0007873725,0.0001500653,0.0003885823,0.0001981343,0.0001582431,0.0002744209],"category_scores_gemma":[0.0006420332,0.0001625441,0.0001515301,0.0006084742,0.0001969702,0.0002108306,0.0001981111,0.0001010745,0.0001080211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003706187,"about_ca_system_score_gemma":0.000539858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07552139,"about_ca_topic_score_gemma":0.1478177,"domain_scores_codex":[0.9998667,0.0000273504,0.00001002643,0.00004046195,0.00003602655,0.00001938514],"domain_scores_gemma":[0.9998247,0.00004817603,0.00003606333,0.00001262477,0.00006655003,0.00001195482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001106962,0.0001302394,0.6115872,0.0003405162,0.0001116278,0.001220041,0.002540615,0.005421941,0.0899256,0.0006942331,0.0008029717,0.2871143],"study_design_scores_gemma":[0.00002282448,0.000116236,0.9734673,0.0000683357,0.00006650394,0.0004759719,0.001767998,0.01522203,0.003833924,0.0002194792,0.004714339,0.00002503047],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934731,0.0007692805,0.001848871,0.0001187049,0.000007945376,0.00003496756,0.0002724127,0.00004591878,0.003428647],"genre_scores_gemma":[0.9947314,0.0003714666,0.004125241,0.0000154295,0.000002968637,0.00001244366,0.0002207773,0.000005231705,0.0005149842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07552139,"threshold_uncertainty_score":0.1501637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03919058966160734,"score_gpt":0.2666017578119898,"score_spread":0.2274111681503825,"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."}}