{"id":"W4285305064","doi":"10.2316/j.2022.206-0730","title":"CULTIVATED LAND SEGMENTATION OF REMOTE SENSING IMAGE BASED ON PSPNET OF ATTENTION MECHANISM, 11-19.","year":2022,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Segmentation; Mechanism (biology); Remote sensing; Food security; Cultivated land; Image segmentation; Geography; Computer vision; Agriculture","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003771108,0.00005802243,0.0001155275,0.0002095231,0.00005883904,0.00002605444,0.00006986781,0.00002028352,0.00005828681],"category_scores_gemma":[0.00004062402,0.00004856776,0.00005019772,0.00008724986,0.00001837219,0.0001042355,0.000007087712,0.0000772787,5.838019e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001322046,"about_ca_system_score_gemma":0.00003534758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003311508,"about_ca_topic_score_gemma":0.00003722555,"domain_scores_codex":[0.9989775,0.00008928732,0.0003376488,0.00006577303,0.0004749162,0.00005487305],"domain_scores_gemma":[0.9991122,0.00007836192,0.0005302188,0.00004235794,0.0002047511,0.00003211563],"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":[0.0003175982,0.00006021683,0.02021936,0.00003768281,0.00008493459,0.00004072832,0.0003604598,0.7826536,0.01276144,0.0001034705,0.0001237794,0.1832368],"study_design_scores_gemma":[0.0007336793,0.0002285863,0.05399184,0.00007192307,0.00002270484,0.00009273047,0.000144089,0.9417738,0.002078405,0.0007724904,0.00003789522,0.00005187541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9287441,0.00001838235,0.07001103,0.0005193357,0.0004873075,0.00005045819,0.00003576497,0.000006586304,0.0001269715],"genre_scores_gemma":[0.9659517,0.00002080131,0.03381083,0.00005800926,0.00004417709,5.169685e-9,0.00009947833,0.000002352736,0.00001264382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1831849,"threshold_uncertainty_score":0.1980536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01205724520408611,"score_gpt":0.2425786139256541,"score_spread":0.2305213687215679,"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."}}