{"id":"W1759826110","doi":"10.1111/j.1466-8238.2010.00551.x","title":"Crop planting dates: an analysis of global patterns","year":2010,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":994,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Wisconsin-Madison; National Aeronautics and Space Administration; U.S. Environmental Protection Agency; National Science Foundation","keywords":"Sowing; Temperate climate; Agronomy; Crop; Evapotranspiration; Precipitation; Latitude; Phenology; Environmental science; Geography; Agroforestry; Biology; Ecology; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0007588179,0.0002570454,0.0002881778,0.003165312,0.0001363019,0.0005204079,0.0002164753,0.000161717,0.00114336],"category_scores_gemma":[0.00215173,0.0001492999,0.0005970642,0.006264842,0.0001685283,0.0004700027,0.0006019492,0.0002248906,0.0002817221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002808165,"about_ca_system_score_gemma":0.0001897574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01188277,"about_ca_topic_score_gemma":0.01213465,"domain_scores_codex":[0.9995995,0.00008701897,0.0000365277,0.0001691464,0.00005730863,0.00005042317],"domain_scores_gemma":[0.9981164,0.0006422649,0.0006586782,0.0002462615,0.0002565623,0.00007989423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004660173,0.00001186485,0.9788478,0.00006950733,0.0002059583,0.00005436663,0.0001836414,0.003367012,0.0007382933,0.0002000274,0.000983597,0.01529143],"study_design_scores_gemma":[0.000002254865,0.00001508741,0.9948143,0.0000101934,0.00002300994,0.00003790439,0.0001699015,0.003179782,0.0001427952,0.00008485983,0.001515416,0.000004615877],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830461,0.000396356,0.001960935,0.00007570094,0.000008103216,0.0000111667,0.01262915,0.0001026513,0.001769991],"genre_scores_gemma":[0.9879827,0.0001679535,0.002159981,0.00001355092,0.000007849643,0.00001707845,0.009452631,0.00002818208,0.0001699701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01188277,"threshold_uncertainty_score":0.02362722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01189140887453299,"score_gpt":0.2456454850315479,"score_spread":0.2337540761570149,"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."}}