{"id":"W4256167981","doi":"10.32920/14636877.v1","title":"Using multispectral airborne LiDAR data for land/water discrimination: a case study at Lake Ontario, Canada","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Remote sensing; Multispectral image; Elevation (ballistics); Ranging; Context (archaeology); Environmental science; Wavelength; Geography; Geodesy; Mathematics; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0004274012,0.0005118842,0.0003423664,0.001107041,0.003066057,0.001221827,0.0009015146,0.0009567373,0.0008622577],"category_scores_gemma":[0.001161524,0.0002900837,0.0002995658,0.003140859,0.0008168739,0.0003671584,0.0004549946,0.0003678563,0.0001691678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01428107,"about_ca_system_score_gemma":0.01283403,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9838441,"about_ca_topic_score_gemma":0.9952402,"domain_scores_codex":[0.9993455,0.00005039959,0.00002825522,0.00007842993,0.0003337552,0.0001635919],"domain_scores_gemma":[0.999092,0.0001621434,0.0000562114,0.0000368134,0.0005569155,0.00009595865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007773216,0.001210413,0.6392175,0.001033666,0.0002789663,0.03209711,0.01408001,0.05515355,0.05637079,0.002009234,0.01063315,0.1871383],"study_design_scores_gemma":[0.0001266328,0.0002267962,0.8135238,0.0001351195,0.0001992193,0.001428404,0.02095586,0.1364999,0.01394332,0.0004703096,0.01232861,0.0001620505],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925219,0.0002666181,0.001537449,0.0003484659,0.000009439036,0.0001486277,0.0005842146,0.00007579964,0.004507409],"genre_scores_gemma":[0.9896209,0.0003119269,0.005605829,0.00008426393,0.000005572823,0.00002702713,0.0005593458,0.00002263123,0.003762426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0161559,"threshold_uncertainty_score":0.1036169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06468930722537307,"score_gpt":0.295250647891674,"score_spread":0.2305613406663009,"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."}}