{"id":"W4281660258","doi":"10.5194/isprs-archives-xliii-b2-2022-531-2022","title":"AUTOMATED GENERATION OF HIGH-QUALITY 3D POINT CLOUDS OF ANTLERS USING LOW-COST RANGE CAMERAS","year":2022,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Antler; Computer science; Artificial intelligence; Computer vision; Segmentation; RGB color model; Outlier; Geography","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":[0.0002741719,0.0008096576,0.0006183915,0.002034649,0.0005004461,0.000944904,0.0007655184,0.0005443626,0.002527356],"category_scores_gemma":[0.000859782,0.0006600268,0.00075351,0.001220512,0.0002705315,0.0007393038,0.001111936,0.0006792332,0.001936793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003249832,"about_ca_system_score_gemma":0.0008293011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003397576,"about_ca_topic_score_gemma":0.008080744,"domain_scores_codex":[0.9993904,0.00004400457,0.00002443833,0.0001355599,0.0003585262,0.00004719587],"domain_scores_gemma":[0.9992053,0.0001137094,0.0001116146,0.0002235695,0.0002962885,0.00004956803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002904517,0.0001451415,0.013519,0.0005580162,0.0001711914,0.0006507091,0.0005607567,0.05661189,0.4622802,0.001821865,0.007761664,0.4556291],"study_design_scores_gemma":[0.00005909037,0.000215204,0.0408428,0.0001063644,0.00005691522,0.001000557,0.0005424033,0.682973,0.253949,0.002732171,0.01735537,0.0001670888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1101475,0.0002778706,0.8794081,0.0000925374,0.00006818226,0.0003124023,0.001516969,0.00525699,0.002919561],"genre_scores_gemma":[0.2701952,0.000377173,0.7236561,0.00004538554,0.00002019356,0.0002439899,0.003040133,0.0004965934,0.001925254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003397576,"threshold_uncertainty_score":0.008454859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02605515383750919,"score_gpt":0.2750912024231201,"score_spread":0.2490360485856109,"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."}}