{"id":"W3158910993","doi":"10.24908/iqurcp.7773","title":"TITAN to Google Earth: Workflow for Data Processing for Mobile Terrestrial LIDAR","year":2017,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Workflow; Computer science; Usability; Point cloud; Raw data; Data processing; Remote sensing; Automatic summarization; Data science; Database; Human–computer interaction; Artificial intelligence; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004307342,0.002661305,0.001274551,0.004795625,0.001915762,0.004383587,0.003500256,0.001315575,0.02219616],"category_scores_gemma":[0.009401591,0.001331622,0.003164724,0.003382614,0.001384569,0.003222028,0.00458345,0.002889958,0.02664543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002022216,"about_ca_system_score_gemma":0.005819126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02921557,"about_ca_topic_score_gemma":0.02505452,"domain_scores_codex":[0.9976755,0.000357319,0.0003177555,0.0004083266,0.0008951479,0.0003458598],"domain_scores_gemma":[0.9953083,0.0009303868,0.0002651969,0.00144685,0.001391218,0.0006580828],"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":[0.001321222,0.0003168784,0.007103874,0.002280081,0.0003562178,0.003100002,0.007978761,0.01096049,0.04020632,0.03392971,0.5876581,0.3047883],"study_design_scores_gemma":[0.0004228385,0.0002561568,0.01329234,0.0007213043,0.0001051664,0.001663743,0.00241994,0.06747434,0.03271242,0.0459214,0.8343099,0.0007005772],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008339727,0.0004921889,0.5896521,0.00128505,0.0004090315,0.002460266,0.02875431,0.3519957,0.01661175],"genre_scores_gemma":[0.06554672,0.001105618,0.7548767,0.001372334,0.0001880683,0.003271449,0.1011379,0.05777476,0.01472634],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02921557,"threshold_uncertainty_score":0.0742535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1983849365427489,"score_gpt":0.4245464293438823,"score_spread":0.2261614928011335,"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."}}