{"id":"W4405786911","doi":"10.1109/iros58592.2024.10801407","title":"Proprioception Is All You Need: Terrain Classification for Boreal Forests","year":2024,"lang":"en","type":"article","venue":"","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Terrain; Taiga; Remote sensing; Computer science; Boreal; Environmental science; Artificial intelligence; Geography; Forestry; Cartography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0002375544,0.001057041,0.0004481129,0.001176135,0.0005146749,0.0006707851,0.0009153033,0.0009082719,0.001951808],"category_scores_gemma":[0.001076391,0.0001645011,0.0007213916,0.00138859,0.0002850305,0.0008841791,0.0007875917,0.0007372824,0.001068602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006137698,"about_ca_system_score_gemma":0.0005571548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03799109,"about_ca_topic_score_gemma":0.1144498,"domain_scores_codex":[0.999712,0.00002384913,0.00002070992,0.00009078396,0.00007690017,0.0000756603],"domain_scores_gemma":[0.9997013,0.00005581831,0.00004623327,0.00007313021,0.00007143697,0.00005205751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001609804,0.001115374,0.2461844,0.001560494,0.0003737228,0.001178901,0.000622811,0.04216488,0.02408645,0.001365024,0.17817,0.5015683],"study_design_scores_gemma":[0.000194681,0.0005795467,0.5360369,0.0005125634,0.0002412373,0.001699612,0.003003054,0.3402901,0.02071724,0.005556712,0.09100099,0.000167228],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8285874,0.005407258,0.01303071,0.001513349,0.0007648143,0.0002631212,0.128272,0.008959468,0.01320188],"genre_scores_gemma":[0.7560516,0.001000657,0.03317327,0.0003798962,0.0001529268,0.00009601566,0.2055239,0.0001767826,0.003444855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03799109,"threshold_uncertainty_score":0.07553995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04697952197846778,"score_gpt":0.2556705809727225,"score_spread":0.2086910589942548,"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."}}