{"id":"W7098376663","doi":"","title":"The Effects of Polygon Boundary Pixels on Image Classification Accuracy","year":2016,"lang":"en","type":"article","venue":"","topic":"Coleoptera Taxonomy and Distribution","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Polygon (computer graphics); Linear discriminant analysis; Discriminant; Contextual image classification; Boundary (topology); Pixel; Pattern recognition (psychology); Land cover","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001007401,0.00005311489,0.00005017257,0.000002602139,0.0001795517,0.00003596086,0.0001175756,0.00003171863,0.00009756832],"category_scores_gemma":[0.0002569561,0.00001068043,0.00004660862,0.00007511284,0.00007085966,0.0001007593,0.00001983229,0.00002393485,0.00005816668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001819408,"about_ca_system_score_gemma":0.000005081676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004137319,"about_ca_topic_score_gemma":0.000130747,"domain_scores_codex":[0.9995481,0.00004452907,0.0001049316,0.0001066171,0.00008709701,0.0001086671],"domain_scores_gemma":[0.9985508,0.001268269,0.00006606731,0.00005043522,0.00003511459,0.00002930181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000281847,0.00002576294,0.001204576,0.000001595789,0.000001710874,1.177575e-7,0.000001891411,2.180776e-9,0.4237402,0.003485641,0.0005722001,0.5709381],"study_design_scores_gemma":[0.0001483885,0.0003473689,0.6154991,0.00003157646,0.000004285997,6.005843e-7,0.00004303645,0.000004214575,0.2618794,0.001337047,0.12063,0.00007504474],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905382,0.00004448196,0.0001090237,0.003962623,0.0001055287,0.000157839,0.000008745786,0.00002629,0.005047265],"genre_scores_gemma":[0.999576,0.00005799284,0.000007107688,0.0000808533,0.0000701392,0.00001733318,0.000007543834,8.495761e-8,0.000182958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6142945,"threshold_uncertainty_score":0.1380985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01437155451676765,"score_gpt":0.2250671681031335,"score_spread":0.2106956135863659,"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."}}