{"id":"W4300894075","doi":"","title":"Télédétection des formations végétales particulières de la forêt amazonienne guyanaise","year":2018,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Amazonian; Vegetation (pathology); Satellite imagery; Geography; Remote sensing; Amazon rainforest; Random forest; Cartography; Plug-in; National park; Forestry; Computer science; Ecology; Archaeology; Artificial intelligence","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":[0.0001721479,0.0003990472,0.0002557888,0.0009476272,0.0005955594,0.00133029,0.0002543009,0.0004931714,0.002356968],"category_scores_gemma":[0.0003227665,0.000222385,0.0003769892,0.000951473,0.0003881197,0.0003519546,0.0004541016,0.0005083767,0.0004331376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0012182,"about_ca_system_score_gemma":0.0005296259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1675356,"about_ca_topic_score_gemma":0.2878921,"domain_scores_codex":[0.9998052,0.00003707409,0.000006052819,0.00005834748,0.00002730709,0.00006605604],"domain_scores_gemma":[0.9998266,0.00008287477,0.00002708602,0.00001598784,0.00003139781,0.00001616791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001229903,0.0001181096,0.5624261,0.001210599,0.0004515969,0.004559211,0.02053813,0.01695293,0.2221836,0.006081551,0.003758797,0.1604894],"study_design_scores_gemma":[0.00002050442,0.00004897514,0.9769949,0.00006711425,0.00003250209,0.0004860786,0.002199255,0.002016037,0.005878548,0.0001333647,0.01210268,0.00002006988],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797992,0.001744739,0.005529654,0.0004451409,0.00003723362,0.00004868301,0.001950466,0.0001424949,0.01030236],"genre_scores_gemma":[0.9851135,0.00059783,0.006371863,0.00004217062,0.00001722653,0.00003128847,0.0009501153,0.00003214946,0.00684391],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1675356,"threshold_uncertainty_score":0.3331209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009765579098029505,"score_gpt":0.2198558433992162,"score_spread":0.2100902643011867,"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."}}