{"id":"W6945063305","doi":"10.21227/ea48-e779","title":"Crop phenology---- ground truth for crop monitoring and yield estimation","year":2024,"lang":"en","type":"dataset","venue":"IEEE DataPort","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Crop; Stage (stratigraphy); Yield (engineering); Phenology; Crop yield; Scale (ratio)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005537025,0.002265961,0.0009851317,0.002294872,0.0006984607,0.00105295,0.002337726,0.001343898,0.01069861],"category_scores_gemma":[0.001911465,0.0004259386,0.000983552,0.004777013,0.00044976,0.0007990021,0.0009281381,0.001043271,0.01829976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003270042,"about_ca_system_score_gemma":0.004149137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4524556,"about_ca_topic_score_gemma":0.6477349,"domain_scores_codex":[0.9992285,0.00005547685,0.00004986319,0.0002429493,0.0002725396,0.0001506514],"domain_scores_gemma":[0.9987205,0.00009922324,0.00006997131,0.0003099395,0.0007048948,0.00009542688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001438139,0.0001102324,0.007636631,0.0006258037,0.00009305802,0.00007457481,0.00005026944,0.004751727,0.0009591238,0.0006204415,0.9671227,0.01781158],"study_design_scores_gemma":[0.0001744603,0.00005543088,0.05157083,0.0003763721,0.00007987757,0.0001734262,0.0002996013,0.01597807,0.003322421,0.001738957,0.9261139,0.0001166776],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001656801,0.0001343019,0.0004537682,0.00005411773,0.00004093631,0.00002122693,0.9956537,0.0009973429,0.0009877281],"genre_scores_gemma":[0.001674528,0.00004256321,0.0007204502,0.00001439071,0.000003171237,0.00002267003,0.9969527,0.00003641014,0.0005332092],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4524556,"threshold_uncertainty_score":0.899644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06432924960867818,"score_gpt":0.3406743254214866,"score_spread":0.2763450758128084,"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."}}