{"id":"W3025622517","doi":"10.3390/rs12101551","title":"Detection of Crop Seeding and Harvest through Analysis of Time-Series Sentinel-1 Interferometric SAR Data","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Seeding; Interferometric synthetic aperture radar; Synthetic aperture radar; Environmental science; Remote sensing; Coherence (philosophical gambling strategy); Interferometry; Radar; Growing season; Meteorology; Geology; Computer science; Mathematics; Geography; Agronomy; Telecommunications; Optics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008403062,0.000103183,0.0002889376,0.0002067023,0.00003190326,0.00001690509,0.0000961046,0.00005814902,0.000006747823],"category_scores_gemma":[0.00009349873,0.0001038352,0.00004784992,0.001340381,0.00005122845,0.0001457668,0.00009066572,0.00008052037,0.000001296592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000150586,"about_ca_system_score_gemma":0.000003994206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007981035,"about_ca_topic_score_gemma":0.000004621002,"domain_scores_codex":[0.9993553,0.00001372731,0.0002550862,0.0001851103,0.00009696974,0.00009376722],"domain_scores_gemma":[0.9994665,0.00006681168,0.00007537553,0.0003137008,0.00004692444,0.00003070889],"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":[0.000007619991,0.000003776776,0.00002520976,0.00009576501,0.0003380821,0.000001178555,0.0003143466,0.00009385092,0.3163707,0.00001087664,0.00003108843,0.6827075],"study_design_scores_gemma":[0.00004426698,0.00001371873,0.0002876506,0.00003801013,0.0003157322,0.0000084752,0.00006684766,0.7292436,0.2600076,0.00002607978,0.009857402,0.0000906273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2454102,0.0002787172,0.7533361,0.00009032137,0.00001530246,0.0000648096,0.00001806051,0.0001421404,0.0006442952],"genre_scores_gemma":[0.7459005,0.0001992886,0.2538156,0.00001264843,0.00002950304,2.505402e-9,0.0000194716,0.00001696146,0.000006040488],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7291497,"threshold_uncertainty_score":0.4234276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02531757784491368,"score_gpt":0.244019687052255,"score_spread":0.2187021092073413,"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."}}