{"id":"W2965110601","doi":"10.3390/data4030114","title":"A New Multi-Temporal Forest Cover Classification for the Xingu River Basin, Brazil","year":2019,"lang":"en","type":"article","venue":"Data","topic":"Fish biology, ecology, and behavior","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Land cover; Geography; Forest cover; Biodiversity; Biodiversity hotspot; Structural basin; Drainage basin; Physical geography; Land use; Remote sensing; Ecology; Cartography; Geology; Geomorphology","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002404087,0.00008050878,0.00008451342,0.000008631579,0.000096571,0.00002130451,0.0007564587,0.00008348314,0.002097367],"category_scores_gemma":[0.0000598748,0.00005405863,0.00002677676,0.00005756459,0.000151781,0.0002297849,0.0003716938,0.00007318571,0.002765242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004116559,"about_ca_system_score_gemma":0.00001730284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008388745,"about_ca_topic_score_gemma":0.002365337,"domain_scores_codex":[0.9992622,0.00002349347,0.0001160054,0.0003393728,0.00007558819,0.0001833023],"domain_scores_gemma":[0.9989622,0.0001053983,0.00006494745,0.0008189594,0.000004428847,0.00004411497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000159767,0.0000406756,0.7922137,0.00000192679,0.000004716719,3.476356e-7,0.00003394574,0.000005099201,0.002869309,0.00002640051,0.2006152,0.004172705],"study_design_scores_gemma":[0.0003366159,0.0000317425,0.6641955,0.000001138836,0.00001478656,0.000001381814,0.00002545438,0.003046596,0.00002445038,0.00003230459,0.3322318,0.00005820699],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9686028,0.00006955651,0.01900399,0.005417728,0.001530497,0.002057436,0.001776913,0.0000806821,0.001460433],"genre_scores_gemma":[0.9817182,0.00001330948,0.00870243,0.001324308,0.000106883,0.00002591346,0.001433929,0.00001279264,0.006662232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1316166,"threshold_uncertainty_score":0.9988148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0699151837982141,"score_gpt":0.3099060129765611,"score_spread":0.239990829178347,"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."}}