{"id":"W3098282109","doi":"","title":"Curiosity Based Exploration for Learning Terrain Models","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Terrain; Perplexity; Computer science; Artificial intelligence; Discriminative model; Path (computing); Motion planning; Machine learning; Robot; Computer vision; Language model; Cartography; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009159871,0.0002526613,0.0002580676,0.0001610305,0.0001957598,0.0003445031,0.001266745,0.0002258905,0.000008020653],"category_scores_gemma":[0.0002205527,0.000253096,0.0001320814,0.000147935,0.00002231184,0.000458687,0.0009438953,0.0006937653,0.00004067043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001367745,"about_ca_system_score_gemma":0.0003583459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006167665,"about_ca_topic_score_gemma":0.00002927372,"domain_scores_codex":[0.9981533,0.0001668258,0.0003018586,0.0008126786,0.0003081752,0.0002571631],"domain_scores_gemma":[0.9980285,0.0001877863,0.0002564578,0.001027425,0.0003601433,0.0001397202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004113379,0.00004769593,0.00008494686,0.00004552642,0.000008685808,2.154679e-7,0.0007501926,0.9530683,0.00004658409,0.03236761,0.001126684,0.01244943],"study_design_scores_gemma":[0.0002328128,0.00003432789,0.00007810536,0.00002386911,0.000007529438,3.267466e-7,0.00001194326,0.854993,0.00009296801,0.1426085,0.001669062,0.0002474894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001884791,0.00002443462,0.985889,0.006424998,0.0002199682,0.0009638346,0.000007820399,0.0009950903,0.003590111],"genre_scores_gemma":[0.5486608,0.000001337967,0.4498564,0.0002527803,0.00008829041,0.0006587284,0.0001366271,0.00001971373,0.0003253024],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5467761,"threshold_uncertainty_score":0.9999921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09872959158289273,"score_gpt":0.3370356043554977,"score_spread":0.2383060127726049,"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."}}