{"id":"W4416354569","doi":"10.48550/arxiv.2511.12341","title":"LiDAR Accuracy on North American Mountain Summits","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Elevation (ballistics); Terrain; Summit; Altitude (triangle); Digital elevation model","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003403645,0.0002259444,0.0002130543,0.001377045,0.0008116048,0.0008451057,0.00036228,0.0001974432,0.0009590833],"category_scores_gemma":[0.001043435,0.0001301981,0.0001363446,0.001973783,0.0001830768,0.0002901846,0.0004499573,0.0001563352,0.0002580989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008377961,"about_ca_system_score_gemma":0.0007301989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3100843,"about_ca_topic_score_gemma":0.6335281,"domain_scores_codex":[0.9997726,0.00001811427,0.00001326627,0.00006811533,0.00008505877,0.0000428999],"domain_scores_gemma":[0.999106,0.00007063375,0.0001025846,0.00004750577,0.0006143261,0.00005895377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006597094,0.00002199811,0.9617319,0.00004279082,0.00005199794,0.0001641922,0.0006662613,0.002686551,0.003692655,0.0001185889,0.001504364,0.02925272],"study_design_scores_gemma":[0.000002674112,0.00000892978,0.9939578,0.00002791234,0.0000127204,0.00004558401,0.0006999641,0.002709019,0.0007516518,0.00003194774,0.001745417,0.000006379661],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942919,0.0002283035,0.000428287,0.00003616387,0.000006191548,0.000006761402,0.001531017,0.00006776472,0.003403618],"genre_scores_gemma":[0.9965106,0.0001198545,0.0008311674,0.0000176588,0.000004470281,0.00000971305,0.0018015,0.000007581886,0.0006974298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3100843,"threshold_uncertainty_score":0.6165589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0263449167520461,"score_gpt":0.2811440418918291,"score_spread":0.254799125139783,"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."}}