{"id":"W2041524230","doi":"10.1016/j.isprsjprs.2014.12.015","title":"A discrepancy measure for segmentation evaluation from the perspective of object recognition","year":2015,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Natural Resources and Forestry; Ontario Forest Research Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Euclidean distance; Object (grammar); Scale-space segmentation; Measure (data warehouse); Segmentation-based object categorization; Image segmentation; Identification (biology); Workflow; Perspective (graphical); Metric (unit); Computer vision; Data mining; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001344875,0.000120192,0.0002067981,0.0001208386,0.0000661251,0.0000549383,0.00005033749,0.00007683588,0.000001181818],"category_scores_gemma":[0.0009896104,0.00008992384,0.0001031628,0.0002289777,0.00006357952,0.0001881839,0.000007025926,0.0001764064,5.634848e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001903857,"about_ca_system_score_gemma":0.0000710012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000358321,"about_ca_topic_score_gemma":0.0001191355,"domain_scores_codex":[0.998827,0.0001675593,0.0003941375,0.0001146495,0.0003763278,0.0001203289],"domain_scores_gemma":[0.9979674,0.0002656925,0.0003106411,0.0001257267,0.001261918,0.00006861324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001370716,0.00001068556,0.00001937064,0.00001997512,0.0001277348,0.000002153133,0.003079226,0.0006443461,0.1490253,0.000002092145,0.0001330922,0.8467989],"study_design_scores_gemma":[0.002481629,0.0002387563,0.001100625,0.0006307736,0.0006344464,0.000219244,0.01663919,0.7409545,0.220305,0.01638648,0.0001725828,0.0002367533],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5129794,0.001652871,0.4838617,0.0002040594,0.000585754,0.0003854505,0.00001058812,0.0000222358,0.0002979912],"genre_scores_gemma":[0.9293163,0.00007716169,0.07028855,0.00001821316,0.0002616649,7.543604e-8,0.00001394641,0.00002185982,0.000002226705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8465621,"threshold_uncertainty_score":0.3666988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06243988039587603,"score_gpt":0.300760192899016,"score_spread":0.2383203125031399,"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."}}