{"id":"W1840686580","doi":"10.5194/isprsarchives-xl-1-w4-313-2015","title":"UAV-BASED POINT CLOUD GENERATION FOR OPEN-PIT MINE MODELLING","year":2015,"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":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Géomatique du Québec; York University; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photogrammetry; Point cloud; Bundle adjustment; Computer science; Structure from motion; Computer vision; Artificial intelligence; Software; Matching (statistics); Inertial measurement unit; Camera resectioning; Calibration; Inertial navigation system; Inertial frame of reference; Motion (physics)","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00248367,0.0003998508,0.0004249182,0.0005417312,0.001472994,0.001265773,0.001933765,0.00009466108,0.00001441128],"category_scores_gemma":[0.0007232805,0.0002422115,0.0003244282,0.0007511286,0.001863143,0.0007231683,0.0004401376,0.0003316887,0.000004796121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002843177,"about_ca_system_score_gemma":0.0003437234,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8721093,"about_ca_topic_score_gemma":0.4508268,"domain_scores_codex":[0.9957499,0.000352637,0.001319254,0.0004333481,0.001623034,0.0005218617],"domain_scores_gemma":[0.9965841,0.0009796504,0.001357054,0.0004246581,0.0004317564,0.0002228337],"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":[0.0002460104,0.00001489733,0.0005833342,0.0000286503,0.00005346614,2.114216e-7,0.002206724,0.06656457,0.0004463607,0.000006125499,0.0001558652,0.9296938],"study_design_scores_gemma":[0.001076022,0.0002382973,0.00159658,0.0002100933,0.00003835897,0.0000636091,0.00186394,0.9816902,0.003036943,0.005276085,0.004590049,0.0003197947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01971655,0.00005710871,0.9650721,0.004159919,0.002690116,0.000905891,0.0002515364,0.00004163172,0.007105127],"genre_scores_gemma":[0.9880099,0.00006372103,0.009881018,0.001442622,0.0002626761,3.94335e-7,0.0001901691,0.000008011939,0.0001415052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9682933,"threshold_uncertainty_score":0.999827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05503380958655039,"score_gpt":0.267273549453317,"score_spread":0.2122397398667666,"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."}}