{"id":"W2118518079","doi":"10.1109/multi-temp.2013.6866019","title":"A multisensor, multitemporal approach for monitoring herbaceous vegetation growth in the Amazon floodplain","year":2013,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Floodplain; Herbaceous plant; Vegetation (pathology); Environmental science; Amazon rainforest; Remote sensing; Hydrology (agriculture); Geography; Cartography; Geology; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000247735,0.000381049,0.0002497777,0.001499871,0.000236302,0.0003916551,0.0002954823,0.0003015053,0.0005251897],"category_scores_gemma":[0.0002052782,0.0002135969,0.0002753678,0.0008969436,0.0000977552,0.0003640996,0.0003082837,0.0001199272,0.00009112273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002979496,"about_ca_system_score_gemma":0.0002990709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008044903,"about_ca_topic_score_gemma":0.02772355,"domain_scores_codex":[0.9998769,0.00001602724,0.000006922638,0.00005185966,0.0000301855,0.00001820445],"domain_scores_gemma":[0.9999378,0.00001109992,0.00001363188,0.000007918757,0.0000175388,0.0000121238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006587306,0.0009530719,0.2053115,0.0002613266,0.0003915464,0.0004840281,0.0004071688,0.05585269,0.2862757,0.0008356869,0.001521263,0.4470473],"study_design_scores_gemma":[0.00004779838,0.0002927341,0.3432104,0.00002165284,0.0001537975,0.0002245432,0.0004452275,0.6370016,0.01626591,0.0005110191,0.00176988,0.0000553417],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9199535,0.0005182766,0.07514627,0.00008510401,0.00002467577,0.0001091289,0.001143135,0.0004667202,0.002553106],"genre_scores_gemma":[0.9068632,0.0001673237,0.09147835,0.00002868936,0.00001806976,0.00006680249,0.0005967167,0.00001152962,0.0007692546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008044903,"threshold_uncertainty_score":0.01599616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01269751708149733,"score_gpt":0.2174789761586973,"score_spread":0.2047814590772,"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."}}