{"id":"W2608264083","doi":"10.3390/rs9050413","title":"Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Biodiversity Monitoring Institute; University of Alberta; University of Calgary","funders":"University of Alberta; Natural Sciences and Engineering Research Council of Canada; Government of Alberta; Alberta-Pacific Forest Industries; Alberta Biodiversity Monitoring Institute; Alberta Innovates; Cenovus Energy; ConocoPhillips","keywords":"Vegetation (pathology); Remote sensing; Environmental science; Photogrammetry; Aerial imagery; Geology","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":[0.0002517432,0.000172249,0.0001136218,0.0007656468,0.0002316575,0.0002764885,0.0002797276,0.0001364868,0.000318133],"category_scores_gemma":[0.000671195,0.00006111384,0.00008359412,0.0006785063,0.0001222927,0.0006638049,0.0002354254,0.000173212,0.00008281757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002535361,"about_ca_system_score_gemma":0.0003095607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01239743,"about_ca_topic_score_gemma":0.04175115,"domain_scores_codex":[0.9997727,0.00005968275,0.00000986296,0.00003589306,0.00009494506,0.00002677851],"domain_scores_gemma":[0.9996018,0.00009061947,0.0001052281,0.00004649847,0.0001152238,0.00004061862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002515135,0.0002966334,0.4911081,0.0002865472,0.0001406677,0.000576312,0.001153513,0.03400374,0.05382184,0.0005670725,0.001144767,0.4166492],"study_design_scores_gemma":[0.00002168059,0.0005295524,0.7200784,0.00008259983,0.0001003409,0.0002541752,0.002758187,0.244132,0.0258125,0.0005785078,0.005595923,0.00005611977],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926909,0.000124756,0.005488499,0.000042077,0.000005719316,0.00004805111,0.0003415018,0.00007953363,0.001179005],"genre_scores_gemma":[0.9919471,0.00008351342,0.007595388,0.000006198225,0.000001812014,0.00001119762,0.0001771453,0.000004632328,0.0001729401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01239743,"threshold_uncertainty_score":0.02465057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03374806968421248,"score_gpt":0.2663592703994442,"score_spread":0.2326112007152317,"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."}}