{"id":"W2056141749","doi":"10.1109/jstars.2014.2344630","title":"Mapping Asian Cropping Intensity With MODIS","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Aeronautics and Space Administration; National Science Foundation","keywords":"Cropping; Agriculture; Environmental science; Scale (ratio); Agricultural productivity; Remote sensing; Geography; Subtropics; Moderate-resolution imaging spectroradiometer; Climatology; Meteorology; Physical geography; Environmental resource management; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000390585,0.0001670376,0.0002926925,0.000111802,0.0001921128,0.00007296036,0.00009610398,0.0001153919,0.000005132865],"category_scores_gemma":[0.00009106623,0.0001244432,0.00003240693,0.0008122901,0.0001234474,0.0001251739,0.00003500774,0.0005146399,0.00000238607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001025086,"about_ca_system_score_gemma":0.00002844888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000161782,"about_ca_topic_score_gemma":0.0005943201,"domain_scores_codex":[0.9986976,0.00005292012,0.0004215489,0.0002109111,0.0003559864,0.0002609729],"domain_scores_gemma":[0.999251,0.00004965189,0.0003005849,0.000153943,0.0001457762,0.00009902793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009247735,0.0000497009,0.007163803,0.00004434948,0.00007038769,0.00006648913,0.003426584,0.0268861,0.3108118,0.0001108449,0.000488028,0.6507894],"study_design_scores_gemma":[0.001206351,0.0001456974,0.8063335,0.0004925327,0.00003934678,0.0009759918,0.0006882837,0.1685712,0.007656891,0.002061277,0.01131146,0.0005174288],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9293309,0.00001019654,0.06466708,0.001107665,0.000155041,0.0001206719,3.082212e-7,0.00002069303,0.004587418],"genre_scores_gemma":[0.7809917,0.00001932737,0.218403,0.0002739033,0.0002055587,9.909081e-9,0.000001227383,0.00001252384,0.00009274426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7991698,"threshold_uncertainty_score":0.5074647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01305166816094133,"score_gpt":0.1930557805189889,"score_spread":0.1800041123580476,"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."}}