{"id":"W4400741276","doi":"10.1080/07038992.2024.2368036","title":"Rice Phenology Classification Model Based on Sentinel-1 Using Machine Learning Method on Google Earth Engine","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Phenology; Remote sensing; Computer science; Artificial intelligence; Machine learning; Environmental science; Geography; Meteorology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006859325,0.0002408712,0.0002639533,0.0003538918,0.0002722173,0.0001391315,0.0001488887,0.0001636188,0.00005673593],"category_scores_gemma":[0.0002530678,0.0002007199,0.0001457297,0.0005595206,0.00009206445,0.0001442728,0.0000153712,0.0009817154,0.00007606878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007200507,"about_ca_system_score_gemma":0.0002277048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003729544,"about_ca_topic_score_gemma":0.002729623,"domain_scores_codex":[0.9982239,0.0002518129,0.0003782447,0.0003395903,0.0003579113,0.0004485469],"domain_scores_gemma":[0.9989285,0.0001636572,0.0002113689,0.000215029,0.00005092103,0.0004305518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001620775,0.000005046611,0.00003884484,0.0000134954,0.00002075358,0.0004069585,0.0002825974,0.69681,0.0898798,0.00001533142,0.0003659334,0.2121451],"study_design_scores_gemma":[0.000172421,0.00007088555,0.0007116211,0.0002902892,0.00005069929,0.0006814715,0.00005326055,0.9845102,0.002393627,0.0001365918,0.01072205,0.0002068985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3861456,0.0001695506,0.5995275,0.003042541,0.0008621078,0.0001470812,0.000005505898,0.00006277572,0.01003735],"genre_scores_gemma":[0.6228112,0.000006535346,0.3758436,0.0005342821,0.0002064679,3.698721e-9,0.000006330057,0.00004279807,0.0005488402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2877002,"threshold_uncertainty_score":0.8185121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02349657835121187,"score_gpt":0.2510326009340184,"score_spread":0.2275360225828065,"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."}}