{"id":"W2727823617","doi":"10.1016/j.jag.2017.06.010","title":"Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure","year":2017,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Coastal wetland ecosystem dynamics","field":"Environmental Science","cited_by":105,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Mangrove; Normalized Difference Vegetation Index; Thematic Mapper; Intertidal zone; Geography; Remote sensing; Digital elevation model; Wetland; Elevation (ballistics); Land cover; Environmental science; Vegetation (pathology); Land use; Ecology; Satellite imagery; Leaf area index; Oceanography; Geology","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":[0.0004310331,0.0001120611,0.0001327275,0.0001031007,0.0002328079,0.0003533099,0.0004245695,0.00006244826,0.00003304433],"category_scores_gemma":[0.0001612773,0.00009975594,0.00002575841,0.00004646578,0.00005865707,0.001653873,0.0003323309,0.0001117295,0.000012242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006127691,"about_ca_system_score_gemma":0.00003407918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007804682,"about_ca_topic_score_gemma":0.001220765,"domain_scores_codex":[0.9987826,0.000006453743,0.0004871346,0.0001372806,0.0004708669,0.000115702],"domain_scores_gemma":[0.9987,0.00004979546,0.0008308523,0.0002420176,0.00009457783,0.00008275252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006578289,0.00008004874,0.4999667,0.00007708178,0.00007573342,0.00001962574,0.00141422,0.03187728,0.003405648,0.0002060064,0.0002442306,0.4619756],"study_design_scores_gemma":[0.0008676453,0.00001166533,0.4602447,0.00005797691,0.000005216513,0.00003466404,0.00009052014,0.5375285,0.00004476046,0.0002678517,0.0007800209,0.00006645116],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9209452,0.000005757955,0.07799519,0.0003042726,0.0002005774,0.0001588913,0.00002264665,0.000008299268,0.0003592111],"genre_scores_gemma":[0.9494487,0.00001500312,0.05017877,0.0002030693,0.0000695436,8.46531e-7,0.00005755882,0.00000688241,0.00001960451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5056512,"threshold_uncertainty_score":0.4067929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03740659256755462,"score_gpt":0.2715000732580019,"score_spread":0.2340934806904473,"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."}}