{"id":"W2147589349","doi":"10.1139/juvs-2013-0014","title":"Small unmanned aircraft: precise and convenient new tools for surveying wetlands","year":2013,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Kenneth M. Molson Foundation; Fonds Québécois de la Recherche sur la Nature et les Technologies; Ministry of Natural Resources","keywords":"Wetland; Vegetation (pathology); Remote sensing; Land cover; Environmental science; Cover (algebra); Georeference; Sampling (signal processing); Scale (ratio); Aerial survey; Vegetation cover; Aerial photography; Vegetation classification; Cohen's kappa; Hydrology (agriculture); Cartography; Geography; Land use; Computer science; Physical geography; Ecology; Geology; Engineering; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.000552376,0.00035046,0.0002126573,0.0006305838,0.0001900629,0.0003067468,0.0003805507,0.0001870304,0.001522898],"category_scores_gemma":[0.0007702374,0.000217448,0.0001760154,0.0003875711,0.0002486918,0.0009337857,0.0004881462,0.0002515367,0.0004061656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001354324,"about_ca_system_score_gemma":0.0001832058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002040186,"about_ca_topic_score_gemma":0.008034864,"domain_scores_codex":[0.9996044,0.0001328659,0.00002211411,0.00007077245,0.0001422758,0.00002750097],"domain_scores_gemma":[0.9994226,0.0001306934,0.0001053048,0.0001576603,0.0001256911,0.0000580668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004130197,0.0001794426,0.06433579,0.0003119757,0.00005988974,0.0004599431,0.0006921877,0.003876535,0.5182492,0.001053947,0.002516086,0.4078519],"study_design_scores_gemma":[0.0001421116,0.003533684,0.6620148,0.0001696108,0.0002207619,0.003908551,0.001449978,0.09697066,0.1559303,0.002464801,0.07302999,0.0001647603],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.675266,0.0009270622,0.3170648,0.0003161838,0.0001115066,0.0004719809,0.001442967,0.0007821468,0.003617362],"genre_scores_gemma":[0.4981023,0.0003272568,0.499392,0.0000886833,0.00004799441,0.0002760721,0.0006727673,0.00005222105,0.001040713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002040186,"threshold_uncertainty_score":0.005094588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.034039878425319,"score_gpt":0.2345993229237723,"score_spread":0.2005594444984533,"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."}}