{"id":"W2895449883","doi":"10.1139/juvs-2016-0023","title":"Photogrammetric surveying forests and woodlands with UAVs: techniques for automatic removal of vegetation and digital terrain model production for hydrological applications","year":2018,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Photogrammetry; Terrain; Digital elevation model; Point cloud; Digital surface; Remote sensing; Vegetation (pathology); Aerial survey; Triangulation; Woodland; Environmental science; Geography; Cartography; Computer science; Lidar; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001804886,0.0003431871,0.0002321725,0.001681386,0.0001848202,0.0003210561,0.000305103,0.0002120491,0.0008584638],"category_scores_gemma":[0.000313199,0.0002067191,0.0002933038,0.001226335,0.0001476449,0.0004113335,0.0003436664,0.0001670351,0.000435632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001661116,"about_ca_system_score_gemma":0.0002143473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002085957,"about_ca_topic_score_gemma":0.005387771,"domain_scores_codex":[0.9998233,0.00003555245,0.000007517433,0.00003554991,0.00007948928,0.00001869809],"domain_scores_gemma":[0.9998566,0.0000314458,0.00002595171,0.00004290064,0.0000357521,0.000007242549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001136528,0.00008809847,0.01963731,0.0002856556,0.00006461103,0.0002603872,0.0003431327,0.02395621,0.2111011,0.001299822,0.002228479,0.7406216],"study_design_scores_gemma":[0.00006339986,0.0003307586,0.2014859,0.000104899,0.0001188774,0.001787878,0.0009673333,0.6029516,0.1665412,0.003534429,0.0220195,0.00009420166],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3100346,0.000756335,0.6832486,0.0000913233,0.00004832977,0.0001241871,0.0008234977,0.001613629,0.003259416],"genre_scores_gemma":[0.602211,0.000436495,0.395272,0.00003074804,0.00001674505,0.00008042464,0.0008183169,0.00008547225,0.001048766],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002085957,"threshold_uncertainty_score":0.004147708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01581873162401253,"score_gpt":0.2548536033918981,"score_spread":0.2390348717678855,"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."}}