{"id":"W4393883050","doi":"10.5281/zenodo.4692366","title":"Supplementary files from Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for peatland mapping and virtual reality (VR) visualization","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"","keywords":"Photogrammetry; Visualization; Computer graphics (images); Lidar; Peat; Computer science; Virtual reality; Structure from motion; Motion (physics); Remote sensing; Computer vision; Geology; Artificial intelligence; Geography; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00112624,0.002506576,0.00145562,0.003219034,0.0008831713,0.002043837,0.002684438,0.002199032,0.2285087],"category_scores_gemma":[0.005662104,0.0007058696,0.001135559,0.005070344,0.0004187753,0.001625,0.001869537,0.00165482,0.1882313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00159517,"about_ca_system_score_gemma":0.001752277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02167547,"about_ca_topic_score_gemma":0.05539641,"domain_scores_codex":[0.9989203,0.0001383281,0.0001337334,0.0003318666,0.0003098301,0.0001659923],"domain_scores_gemma":[0.9973797,0.0009569311,0.0001881475,0.0004631312,0.0007953421,0.0002169651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000320954,0.00002864119,0.0004279545,0.0004959932,0.00001526109,0.0000201841,0.00001459335,0.0003483749,0.00009416553,0.0003424038,0.9962819,0.001898453],"study_design_scores_gemma":[0.0002545726,0.00002145929,0.004134623,0.0003716342,0.00002873638,0.00008666231,0.000112124,0.0008075609,0.0005328108,0.002309151,0.9913027,0.00003790328],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006293782,0.00001793959,0.00006966708,0.00002135003,0.00001457428,0.00000761325,0.999172,0.0002220521,0.0004118617],"genre_scores_gemma":[0.0002034396,0.00001707189,0.0003203522,0.00001884769,0.000003287821,0.00004820869,0.9988998,0.00008414602,0.0004048915],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2285087,"threshold_uncertainty_score":0.7644378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02848086240381861,"score_gpt":0.2529330297582189,"score_spread":0.2244521673544002,"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."}}