{"id":"W2910961990","doi":"10.3390/data4010009","title":"UAV-Based 3D Point Clouds of Freshwater Fish Habitats, Xingu River Basin, Brazil","year":2019,"lang":"en","type":"article","venue":"Data","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":9,"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":"Point cloud; Tributary; Habitat; Geography; Amazon rainforest; Drainage basin; Structure from motion; Environmental science; Ecology; Hydrology (agriculture); Geology; Cartography; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001155897,0.0003523956,0.0002537419,0.001865098,0.0002625723,0.0004728344,0.0003007485,0.0002754758,0.0008700876],"category_scores_gemma":[0.0004556885,0.0002734963,0.0003676747,0.001873381,0.0002166287,0.0003434486,0.0005160829,0.000158506,0.0002335383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004894231,"about_ca_system_score_gemma":0.0005830542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08778815,"about_ca_topic_score_gemma":0.1426251,"domain_scores_codex":[0.9998602,0.00001245532,0.00001453693,0.00004194194,0.00004410279,0.00002679588],"domain_scores_gemma":[0.9998571,0.00002363503,0.0000241934,0.00002628489,0.00005376557,0.00001498225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002928234,0.0002150001,0.4391466,0.0007037551,0.0003009485,0.002355983,0.002063602,0.2865883,0.05274794,0.001766275,0.005727147,0.2080916],"study_design_scores_gemma":[0.00005142744,0.00006309103,0.6490025,0.0001206673,0.00008564822,0.0005456885,0.001946506,0.3345,0.005710668,0.0006647127,0.007234937,0.00007425799],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825665,0.0003089102,0.006480749,0.0000664064,0.00001400044,0.00007348961,0.007589018,0.0003692264,0.002531557],"genre_scores_gemma":[0.9874682,0.0001756408,0.007600918,0.000008573819,0.0000033168,0.00003705535,0.00442734,0.00001456445,0.0002643426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08778815,"threshold_uncertainty_score":0.1745543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01471929793077629,"score_gpt":0.244877636669857,"score_spread":0.2301583387390807,"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."}}