{"id":"W2766993377","doi":"10.1117/12.2277968","title":"Applying a particle filtering technique for canola crop growth stage estimation in Canada","year":2017,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; AUG Signals (Canada)","funders":"","keywords":"Synthetic aperture radar; Stage (stratigraphy); Computer science; Remote sensing; Ground truth; Polarimetry; Canola; Artificial intelligence; Geography; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001029289,0.0001013587,0.0001147273,0.00002900695,0.0001315806,0.00005317407,0.0001951417,0.0000403714,0.00002697815],"category_scores_gemma":[0.00004446803,0.00009940232,0.00001946266,0.00003996872,0.00001434765,0.0001151946,0.00002845502,0.00006630403,0.000001279471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002741546,"about_ca_system_score_gemma":0.00007984094,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3860784,"about_ca_topic_score_gemma":0.4793231,"domain_scores_codex":[0.9994336,0.000003712553,0.0001663435,0.0001278086,0.00007178551,0.0001967435],"domain_scores_gemma":[0.9995189,0.00004541903,0.00003323468,0.0003392604,0.00002213531,0.00004102134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001633739,0.00004674081,0.00645183,0.000462683,0.00004427248,0.00001536252,0.0001720797,0.001038317,0.1310292,0.03290676,0.003096259,0.8247202],"study_design_scores_gemma":[0.0001573455,0.000008580062,0.001583315,0.00004684917,0.000004844661,0.000003856361,0.00003698335,0.2435985,0.7087952,0.0009760375,0.04457651,0.0002119146],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01986795,0.00001927113,0.9745256,0.0001363248,0.00003513564,0.0009969932,0.00001623616,0.0001898149,0.004212697],"genre_scores_gemma":[0.6855433,0.000006371456,0.3128802,0.00002188256,0.000008871777,0.001455634,0.00000269599,0.00001831197,0.00006273687],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8245082,"threshold_uncertainty_score":0.6180098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01284889281471255,"score_gpt":0.2390099321245072,"score_spread":0.2261610393097947,"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."}}