{"id":"W2079800045","doi":"10.3390/rs6053583","title":"An Algorithm for Boundary Adjustment toward Multi-Scale Adaptive Segmentation of Remotely Sensed Imagery","year":2014,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Image resolution; Artificial intelligence; Computer vision; Segmentation; Pixel; Image segmentation; Geospatial analysis; Remote sensing; Homogeneity (statistics); Boundary (topology); Geology; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001935285,0.0002157224,0.0002956607,0.0001324326,0.00007289703,0.00002327645,0.00007196789,0.00009595192,0.000003084839],"category_scores_gemma":[0.00003813647,0.0002380307,0.0000937803,0.0001241413,0.00006574328,0.0002421245,0.00002363105,0.000129718,0.000003531341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001650837,"about_ca_system_score_gemma":0.00001842635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003577227,"about_ca_topic_score_gemma":0.000008116591,"domain_scores_codex":[0.9988615,0.00005999516,0.0003523942,0.0002673017,0.0001783113,0.0002804961],"domain_scores_gemma":[0.999229,0.00009446694,0.0001047193,0.0003140133,0.0001718163,0.00008594739],"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":[0.00000993615,0.000007946063,1.861122e-7,0.00003530734,0.00000913013,0.000001675708,0.0002784526,0.00116799,0.4014574,6.598636e-7,0.00005805421,0.5969733],"study_design_scores_gemma":[0.0003340013,0.00008738755,0.00004873268,0.00006840225,0.00001719521,0.00001331509,0.0001885155,0.6137406,0.3848467,0.000266045,0.0002340694,0.0001550399],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01473002,0.0001208597,0.9834628,0.00001400252,0.0002847921,0.0005077761,0.00002136551,0.0006307146,0.0002276807],"genre_scores_gemma":[0.06065322,0.00005142136,0.9389262,0.00006171937,0.0001566089,1.497643e-7,0.00003929176,0.00007894556,0.0000323978],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6125726,"threshold_uncertainty_score":0.9706609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01873969941610128,"score_gpt":0.2744140504497706,"score_spread":0.2556743510336694,"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."}}