{"id":"W2082353960","doi":"10.1109/wacv.2015.86","title":"The Mountain Habitats Segmentation and Change Detection Dataset","year":2015,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Segmentation; Computer science; Ground truth; Artificial intelligence; Change detection; Baseline (sea); Image segmentation; Habitat; Classifier (UML); Computer vision; Pattern recognition (psychology); Remote sensing; Geography; Ecology; Geology","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":[0.0005915911,0.001732895,0.001195624,0.003494633,0.001575547,0.001216611,0.003098232,0.002380599,0.004660624],"category_scores_gemma":[0.001376627,0.0004205494,0.001449037,0.004932906,0.0006162382,0.000762997,0.00125118,0.001423072,0.004748252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001545273,"about_ca_system_score_gemma":0.002050454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07403952,"about_ca_topic_score_gemma":0.1753247,"domain_scores_codex":[0.9990747,0.00007487991,0.00006965929,0.0002990529,0.0003003709,0.0001812904],"domain_scores_gemma":[0.9992232,0.00009475822,0.00006784877,0.0002109545,0.0002641956,0.0001389046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001104662,0.001374788,0.02725983,0.002332926,0.0005042571,0.001762507,0.0004794372,0.01399843,0.02255332,0.001942676,0.7733351,0.153352],"study_design_scores_gemma":[0.0007923224,0.0004632758,0.25419,0.0003778879,0.0003117779,0.003588171,0.001398021,0.05904866,0.02172825,0.002478123,0.6553308,0.0002928025],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1094417,0.001904777,0.0091808,0.0006624582,0.0002290786,0.00118578,0.8577514,0.009934349,0.009709654],"genre_scores_gemma":[0.03059828,0.0002280868,0.01502962,0.000101257,0.00004071137,0.0003537304,0.9519016,0.0001586432,0.001588083],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07403952,"threshold_uncertainty_score":0.1472171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06172988892440423,"score_gpt":0.2692866749323065,"score_spread":0.2075567860079022,"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."}}