{"id":"W2185241015","doi":"","title":"THE COMMERCIALIZATION OF REMOTE SENSING AND GIS FOR VINEYARD MANAGEMENT: A SIMPLE BUT POWERFUL APPLICATION OF CHANGE DETECTION","year":2008,"lang":"en","type":"article","venue":"","topic":"Horticultural and Viticultural Research","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vineyard; Remote sensing; Commercialization; NAPA; Change detection; Terroir; Geography; Frost (temperature); Environmental resource management; Environmental science; Wine; Meteorology; Archaeology; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001835559,0.0004940882,0.0003274182,0.001825146,0.0003849585,0.001691124,0.0008937275,0.0005557964,0.002504685],"category_scores_gemma":[0.002548998,0.0003467122,0.0004032505,0.001960954,0.0007537984,0.001707199,0.001002004,0.0007893231,0.0005726541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007257139,"about_ca_system_score_gemma":0.0005070816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007983643,"about_ca_topic_score_gemma":0.01159605,"domain_scores_codex":[0.9989551,0.0002218647,0.00004185908,0.0002057091,0.0005348915,0.00004048293],"domain_scores_gemma":[0.9968353,0.001673054,0.0002274932,0.000437119,0.0007023836,0.0001247411],"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":[0.00008009208,0.00007076761,0.01542769,0.0004077209,0.00006888018,0.000304578,0.0008508395,0.002665242,0.02689273,0.006382636,0.006880606,0.9399681],"study_design_scores_gemma":[0.000113684,0.0008328524,0.1301821,0.0006210493,0.000198874,0.003997619,0.002863303,0.0434214,0.06774721,0.01915238,0.7304307,0.0004388079],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.249799,0.02622992,0.5950273,0.0121426,0.0008391112,0.0008346625,0.003377586,0.005956057,0.1057936],"genre_scores_gemma":[0.4682417,0.009956713,0.5044352,0.000610054,0.0004421345,0.0001577019,0.00144303,0.0004251396,0.01428838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007983643,"threshold_uncertainty_score":0.01587439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0809083422722275,"score_gpt":0.2953630402554563,"score_spread":0.2144546979832288,"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."}}