{"id":"W3118725771","doi":"","title":"An Improved Precipitation Indicator For Corn Yield Prediction: Proof Of Concept Using STICS Model In Eastern Canada","year":2018,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Yield (engineering); Precipitation; Agronomy; Proof of concept; Environmental science; Mathematics; Agricultural engineering; Computer science; Geography; Meteorology; Engineering; Materials science; Biology; Metallurgy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009023332,0.0006427872,0.0005946468,0.0004081351,0.0008320144,0.001010472,0.001259422,0.0004712581,0.001122573],"category_scores_gemma":[0.001643751,0.0003081259,0.0005512804,0.000628447,0.0003386304,0.0005369467,0.0004308944,0.0007369933,0.0002030516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004456253,"about_ca_system_score_gemma":0.01130251,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9309273,"about_ca_topic_score_gemma":0.8993396,"domain_scores_codex":[0.9997578,0.00003378273,0.0000106845,0.0000703302,0.00005772523,0.00006969176],"domain_scores_gemma":[0.9991543,0.0001519748,0.00003107296,0.00004402826,0.0005553533,0.00006317616],"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":[0.0003143956,0.0001984622,0.05576152,0.0000390928,0.0001170498,0.00006783997,0.00003066103,0.9082565,0.001963252,0.0009732823,0.002206217,0.0300717],"study_design_scores_gemma":[0.00001712753,0.000009230881,0.006383045,0.000001970883,0.00001575273,0.000002604917,0.00001499647,0.9929228,0.0003944104,0.00007851166,0.0001512384,0.000008334334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664918,0.0002608281,0.02696302,0.0004559651,0.00005421144,0.00005538504,0.001946207,0.0008265576,0.002945982],"genre_scores_gemma":[0.9883366,0.00007853275,0.009119277,0.00003010817,0.00001019445,0.00001455946,0.001125093,0.00003551946,0.001250009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06907272,"threshold_uncertainty_score":0.138959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02686271360206552,"score_gpt":0.2516109333500886,"score_spread":0.224748219748023,"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."}}