{"id":"W4303980543","doi":"10.3390/s22197428","title":"An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; University of Guelph","keywords":"Random forest; Feature selection; Support vector machine; Computer science; Artificial intelligence; Feature (linguistics); Data mining; Classifier (UML); Pattern recognition (psychology); Decision tree; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00160426,0.0006579859,0.0006476386,0.0009106771,0.0004255678,0.0007454327,0.0008373911,0.0007566596,0.0007249387],"category_scores_gemma":[0.001515892,0.0002454128,0.000656665,0.0007883268,0.0003927169,0.001643079,0.001305461,0.0005404737,0.0003094492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003090567,"about_ca_system_score_gemma":0.0003921466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001365255,"about_ca_topic_score_gemma":0.001035715,"domain_scores_codex":[0.9990909,0.0001967971,0.00004340169,0.0002443214,0.0002852569,0.0001394223],"domain_scores_gemma":[0.9994767,0.0001066795,0.0000680697,0.00009854235,0.0002223547,0.00002771415],"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":[0.0007837231,0.0002339712,0.01058607,0.0002572656,0.0001872583,0.0005367738,0.0005806168,0.2464204,0.2849254,0.01243159,0.002281742,0.4407752],"study_design_scores_gemma":[0.00002978589,0.0003056749,0.006035338,0.00002328669,0.00009348859,0.00031758,0.0001170172,0.905518,0.07758526,0.006101536,0.003809618,0.00006344193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1164102,0.0002267759,0.8789174,0.0001463913,0.00004666777,0.00007613035,0.000138043,0.001195784,0.002842585],"genre_scores_gemma":[0.8021727,0.00008611326,0.1969753,0.00004009572,0.00001845407,0.00005062212,0.0001380998,0.00003458848,0.0004840692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00160426,"threshold_uncertainty_score":0.008484244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01270895425572412,"score_gpt":0.2353667314066524,"score_spread":0.2226577771509283,"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."}}