{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006586747,0.00008440204,0.0001872532,0.00009878859,0.0001339685,0.00005416486,0.00006870522,0.0000562916,2.369949e-7],"category_scores_gemma":[0.00008035351,0.00006104794,0.00003451261,0.0002174411,0.0001690256,0.0002064709,0.00001672038,0.00005157022,3.262884e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003959697,"about_ca_system_score_gemma":0.00001297578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002126541,"about_ca_topic_score_gemma":0.00002633734,"domain_scores_codex":[0.9991909,0.00002602636,0.0003431996,0.0001606789,0.0001583522,0.0001208663],"domain_scores_gemma":[0.9992437,0.0001016928,0.0003862383,0.0001119633,0.00009693935,0.00005944597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000816873,0.0007176701,0.05346278,0.001116719,0.0002285198,0.000003503386,0.003451052,0.01248315,0.1258586,0.0005642507,0.0007548154,0.800542],"study_design_scores_gemma":[0.0008664645,0.001693613,0.01734563,0.0001845521,0.00008096286,0.0006505661,0.0002753934,0.9701354,0.004826429,0.002485477,0.001264605,0.0001908975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7664496,0.00006053504,0.2324433,0.00009123615,0.0000175333,0.0007835236,0.000005013354,0.00001758577,0.0001316851],"genre_scores_gemma":[0.978694,0.00000574919,0.02110943,0.000006332861,0.0001001357,0.00003401034,0.000003682251,0.00001123536,0.00003539013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9576523,"threshold_uncertainty_score":0.2489463,"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."}}