{"id":"W4409896704","doi":"10.3390/rs17091539","title":"Mapping Trails and Tracks in the Boreal Forest Using LiDAR and Convolutional Neural Networks","year":2025,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Lidar; Remote sensing; Taiga; Convolutional neural network; Environmental science; Boreal; Geography; Computer science; Forestry; Artificial intelligence; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004122592,0.0005354913,0.0001731669,0.00191739,0.0005950726,0.0007184428,0.0005680773,0.000248196,0.0005323424],"category_scores_gemma":[0.001047784,0.0002080616,0.0003078463,0.001487544,0.0002816289,0.0004781648,0.0004661648,0.0002339838,0.000184619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001601113,"about_ca_system_score_gemma":0.00184327,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7317995,"about_ca_topic_score_gemma":0.8639742,"domain_scores_codex":[0.999726,0.00002219591,0.00001062657,0.00007755519,0.00006778471,0.00009578522],"domain_scores_gemma":[0.999627,0.00006436901,0.00004839964,0.00003278205,0.0001831472,0.00004435554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005911851,0.0002317751,0.4821605,0.000194542,0.0002837465,0.0006522445,0.0007239318,0.1326953,0.01997859,0.0007926446,0.007249194,0.3544463],"study_design_scores_gemma":[0.00003518823,0.00008800058,0.5691703,0.00006632618,0.0001144157,0.0002436785,0.001455596,0.4170142,0.004935009,0.001089551,0.0057282,0.00005959695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818148,0.0005451018,0.01038693,0.0001402677,0.00003054621,0.00004443282,0.002862454,0.0005822009,0.003593209],"genre_scores_gemma":[0.9794714,0.000207,0.01552683,0.00002915385,0.000006989542,0.0000118452,0.003532198,0.00002497005,0.00118944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7317995,"threshold_uncertainty_score":0.5395598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01490210249180167,"score_gpt":0.2450038615049537,"score_spread":0.2301017590131521,"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."}}