{"id":"W1844741564","doi":"10.1109/igarss.1995.521715","title":"Combined analysis of SAR C and TM/Landsat data in the assessment of aquatic vegetation changes in the Tucurui reservoir, Para State, Brazilian Amazon","year":2002,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vegetation (pathology); Amazon rainforest; Remote sensing; Environmental science; Mosaic; Stage (stratigraphy); Aquatic plant; Aquatic ecosystem; Hydrology (agriculture); Geography; Geology; Ecology; Oceanography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003377029,0.0001737919,0.0001803244,0.0007835391,0.0001670008,0.0003541001,0.0001384915,0.0001426088,0.0003091216],"category_scores_gemma":[0.0009174432,0.0001439273,0.0001136967,0.0007171213,0.0001524943,0.0002030936,0.0001906733,0.0001058383,0.00006807688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004046539,"about_ca_system_score_gemma":0.0003285905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05296551,"about_ca_topic_score_gemma":0.1348678,"domain_scores_codex":[0.9998428,0.00005105605,0.00001024226,0.00002160427,0.00004398538,0.0000302851],"domain_scores_gemma":[0.999747,0.00007134082,0.00004484006,0.00001517376,0.00008872923,0.00003291458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003755826,0.0001575564,0.9058023,0.00007241083,0.00008200364,0.0004989577,0.0005878967,0.002484356,0.04518282,0.00008638741,0.000171199,0.04449869],"study_design_scores_gemma":[0.00001100157,0.00009796415,0.9891365,0.000008932042,0.00004037608,0.0001345719,0.0006997557,0.007723682,0.001804079,0.0000295921,0.0003066857,0.000006886361],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993532,0.00004540172,0.0001423258,0.00001458517,7.074447e-7,0.000004098465,0.0000416379,0.000004071708,0.0003939281],"genre_scores_gemma":[0.9994556,0.00003636312,0.0003184446,0.000003536885,0.000001373483,0.000002829972,0.00007509958,0.000001434539,0.0001052103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05296551,"threshold_uncertainty_score":0.1053144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06451611387013048,"score_gpt":0.3058019987464012,"score_spread":0.2412858848762707,"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."}}