{"id":"W2609925010","doi":"10.1007/s11119-017-9521-x","title":"Maximizing the relationship of yield to site-specific management zones with object-oriented segmentation of hyperspectral images","year":2017,"lang":"en","type":"article","venue":"Precision Agriculture","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Program for New Century Excellent Talents in University; Northeast Agricultural University","keywords":"Hyperspectral imaging; Precision agriculture; Scale (ratio); Remote sensing; Segmentation; Multispectral image; Normalized Difference Vegetation Index; Vegetation (pathology); Pixel; Image segmentation; Computer science; Mathematics; Environmental science; Artificial intelligence; Cartography; Geography; Leaf area index; Agronomy; Agriculture","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":[0.0002425481,0.0001766596,0.0001769629,0.0000318492,0.0004126587,0.00006805296,0.0004363334,0.00007904099,0.0001214956],"category_scores_gemma":[0.00011706,0.00008275174,0.00007499122,0.0002595184,0.0001434253,0.0002031635,0.0001999351,0.0001689346,0.00007583966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008369423,"about_ca_system_score_gemma":0.000002327924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001036093,"about_ca_topic_score_gemma":0.00009625297,"domain_scores_codex":[0.9985339,0.00004224699,0.0002722912,0.0003420236,0.0006235513,0.000186003],"domain_scores_gemma":[0.9987738,0.0001603112,0.000353861,0.0005904016,0.00005582934,0.00006584081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003191897,0.0002858688,0.1449888,0.00004514283,0.00007547929,0.00001823559,0.006930317,0.01706536,0.7544999,0.0009225431,0.05809535,0.01675383],"study_design_scores_gemma":[0.0003013237,0.00009969376,0.9532039,0.0001596612,0.00004152942,0.00001468942,0.001261877,0.00002037575,0.04271447,0.0001804031,0.001843334,0.0001587935],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797072,0.00005250796,0.003358413,0.001142258,0.0001477885,0.0007537017,0.0000140657,0.00002972011,0.01479436],"genre_scores_gemma":[0.9615188,0.00003701939,0.03643889,0.00004132433,0.00004057109,0.000006126066,0.00001000605,0.000008890881,0.001898329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.808215,"threshold_uncertainty_score":0.3374518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179329276251935,"score_gpt":0.235580945571732,"score_spread":0.2176480179465385,"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."}}