{"id":"W2529351729","doi":"10.22119/ijte.2013.3233","title":"Development of an Automatic Land Use Extraction System in Urban Areas using VHR Aerial Imagery and GIS Vector Data","year":2013,"lang":"en","type":"article","venue":"","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Geographic information system; Land cover; Computer science; Data mining; Support vector machine; Remote sensing; Land use; Extraction (chemistry); Cartography; Geography; Artificial intelligence; Civil engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0005118328,0.0003665108,0.0003097166,0.001264871,0.0003213609,0.0005321844,0.0004178221,0.0003248522,0.001548824],"category_scores_gemma":[0.000686776,0.000243276,0.0003346363,0.0006601731,0.0001253771,0.0006355119,0.000330791,0.0002649495,0.00118197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002924092,"about_ca_system_score_gemma":0.0006407392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005304395,"about_ca_topic_score_gemma":0.007891294,"domain_scores_codex":[0.9997844,0.00003509127,0.00001929112,0.00006281839,0.00007942989,0.00001900715],"domain_scores_gemma":[0.9997059,0.00004696325,0.00003317982,0.00004306657,0.0001573251,0.00001361974],"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":[0.0001042878,0.0002000836,0.0205532,0.0001964339,0.00007738149,0.0002544064,0.0002335934,0.02611486,0.1356869,0.001933822,0.006342857,0.8083021],"study_design_scores_gemma":[0.000052063,0.0001900851,0.06383572,0.00007042324,0.00008744009,0.000418042,0.0003720496,0.7853749,0.1243687,0.001789694,0.02336504,0.00007594615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1116794,0.00009800253,0.8742793,0.0001070053,0.00003269149,0.000398008,0.001349647,0.008547305,0.003508608],"genre_scores_gemma":[0.2197879,0.00008160024,0.7752423,0.00005903831,0.00001231652,0.0002212123,0.002047949,0.0001308698,0.002416852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005304395,"threshold_uncertainty_score":0.01054704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03277325627154032,"score_gpt":0.241196307262584,"score_spread":0.2084230509910437,"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."}}