{"id":"W2530889941","doi":"10.12789/geocanj.2016.43.099","title":"Remote Predictive Mapping 7. The Use of Topographic–Bathymetric Lidar to Enhance Geological Structural Mapping in Maritime Canada","year":2016,"lang":"en","type":"article","venue":"Geoscience Canada","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Community College","funders":"Fisheries and Oceans Canada; Atlantic Canada Opportunities Agency; Australian Government","keywords":"Geology; Outcrop; Bathymetry; Lidar; Bedrock; Reef; Shore; Submarine pipeline; Geomorphology; Remote sensing; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0002456326,0.0003020004,0.0001226757,0.001221794,0.0006631153,0.001072734,0.0005999142,0.0002037836,0.002940883],"category_scores_gemma":[0.0006231059,0.0001786319,0.0001764161,0.00156538,0.0002916469,0.0003751858,0.0005121368,0.0002731789,0.0005353469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004502702,"about_ca_system_score_gemma":0.006561554,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9512324,"about_ca_topic_score_gemma":0.9710198,"domain_scores_codex":[0.9998623,0.000009971876,0.000004066058,0.00002183424,0.00007157382,0.00003027951],"domain_scores_gemma":[0.9997855,0.0000257516,0.0000129083,0.00001045177,0.0001461256,0.00001938682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001547159,0.0001441486,0.1412636,0.000280112,0.00007800106,0.0008221515,0.0009608136,0.1159809,0.03224399,0.005110929,0.01630302,0.6866577],"study_design_scores_gemma":[0.00006701526,0.00006640636,0.2705501,0.0001813814,0.00008193751,0.0002570511,0.00269144,0.6523166,0.01252447,0.002527996,0.05863585,0.00009967957],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7480385,0.002550081,0.1267991,0.00220724,0.0001141926,0.0004225447,0.01153827,0.004377929,0.1039521],"genre_scores_gemma":[0.9115613,0.0008830574,0.07266793,0.0001068312,0.00001251244,0.00004229445,0.00237358,0.0001028854,0.01224969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04876763,"threshold_uncertainty_score":0.0981096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01777046636373094,"score_gpt":0.2089453565310417,"score_spread":0.1911748901673108,"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."}}