{"id":"W1579588170","doi":"10.5539/jas.v7n8p95","title":"A Stratified Temporal Spectral Mixture Analysis Model for Mapping Cropland Distribution through MODIS Time-Series Data","year":2015,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Endmember; Pixel; Thematic map; Remote sensing; Scale (ratio); Thematic Mapper; Vegetation (pathology); Data set; Time series; SMA*; Environmental science; Computer science; Mathematics; Geography; Statistics; Cartography; Artificial intelligence; Satellite imagery; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008841783,0.0006128638,0.0003511223,0.000750161,0.0002457146,0.0005878777,0.0008755921,0.0003296907,0.0007204899],"category_scores_gemma":[0.001290345,0.0003669709,0.000932412,0.000709846,0.0002146197,0.0008682345,0.0004177456,0.0004280546,0.0003264706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000481922,"about_ca_system_score_gemma":0.0007026731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01282038,"about_ca_topic_score_gemma":0.01203381,"domain_scores_codex":[0.9997055,0.00008746827,0.00001729085,0.00009298597,0.00006328581,0.00003340492],"domain_scores_gemma":[0.9997464,0.0001021335,0.00003833967,0.00002373537,0.00007659809,0.00001275946],"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":[0.0002128876,0.0001119907,0.01324727,0.00006435257,0.0002553367,0.0001077533,0.0001553593,0.8484454,0.01315071,0.006969822,0.0008229885,0.1164562],"study_design_scores_gemma":[0.000001324454,0.00001066786,0.000970297,0.000001592217,0.000009070071,0.00000941788,0.000006373314,0.9977837,0.0003515244,0.0006640589,0.0001875787,0.000004359116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08951292,0.0001730784,0.9090043,0.00006297292,0.00002356823,0.00004754683,0.0001715527,0.0003636949,0.0006403928],"genre_scores_gemma":[0.8075032,0.0002797174,0.1887462,0.00003912949,0.00002798657,0.0002007958,0.0007501771,0.00007587424,0.002377008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01282038,"threshold_uncertainty_score":0.02549154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03762546183102004,"score_gpt":0.2585553465675257,"score_spread":0.2209298847365057,"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."}}