{"id":"W7039182593","doi":"","title":"Mapping coastal Great Lakes wetlands and adjacent land use through hybrid optical-infrared and radar image classification techniques","year":2013,"lang":"en","type":"article","venue":"Digital Commons - Michigan Tech (Michigan Technological University)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wetland; Phragmites; Synthetic aperture radar; Land cover; Thematic Mapper; Land use; Baseline (sea); Flood myth; Ancillary data; Sensor fusion","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008123482,0.0004319115,0.000392007,0.0001886504,0.0003931141,0.0005061363,0.0005254909,0.0003501157,0.00004055913],"category_scores_gemma":[0.0001451289,0.0003534233,0.00008779216,0.0007212884,0.001807052,0.001710599,0.001178871,0.000606091,0.00006132011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007676388,"about_ca_system_score_gemma":0.000008771617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009132427,"about_ca_topic_score_gemma":0.0003997988,"domain_scores_codex":[0.9980571,0.00005141224,0.0002880851,0.0007965443,0.0002879339,0.0005189034],"domain_scores_gemma":[0.9989102,0.0001447771,0.0001604925,0.0005369857,0.00004047933,0.0002070571],"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":[0.0001750962,0.0009604389,0.3069372,0.00009944675,0.0002225798,0.0008347791,0.0009559024,0.000004395063,0.5216465,0.00984147,0.005128323,0.1531939],"study_design_scores_gemma":[0.003035558,0.001247805,0.4997089,0.0004207297,0.0001844142,0.001347587,0.009328147,0.001363901,0.1288934,0.0276858,0.3227406,0.004043153],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9637268,0.00002028322,0.002827447,0.00108814,0.00001994771,0.0006838313,0.00009626382,0.001171264,0.03036601],"genre_scores_gemma":[0.9823365,0.0001417729,0.01605855,0.0000915535,0.00001324702,0.000002170634,0.0001100274,0.00002730183,0.001218927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3927531,"threshold_uncertainty_score":0.9998918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282929982387104,"score_gpt":0.1805860877801284,"score_spread":0.1677567879562573,"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."}}