{"id":"W1965842988","doi":"10.1007/s00484-015-0960-7","title":"Determining the influence of Itaipu Lake on thermal conditions for soybean development in adjacent lands","year":2015,"lang":"en","type":"article","venue":"International Journal of Biometeorology","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Transect; Phenology; Environmental science; Shore; Air temperature; Physical geography; Hydrology (agriculture); Geostatistics; Wind speed; Atmospheric sciences; Spatial variability; Geography; Meteorology; Ecology; Geology; Biology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002095114,0.0001801637,0.0002045876,0.0004878427,0.0005013561,0.0005615231,0.000236589,0.0002328486,0.0006183847],"category_scores_gemma":[0.0008770328,0.0001918064,0.0003668331,0.0006237229,0.000322011,0.0003805232,0.0004822876,0.0001677925,0.0000666474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008907019,"about_ca_system_score_gemma":0.0005406517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02555968,"about_ca_topic_score_gemma":0.07523436,"domain_scores_codex":[0.9998654,0.00003006242,0.000009710653,0.0000344864,0.00001804822,0.0000423483],"domain_scores_gemma":[0.999665,0.0001044594,0.00009747305,0.00002052755,0.00005024723,0.00006225918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001491528,0.00004785303,0.9799293,0.0000304155,0.00007031137,0.0002911766,0.0005273289,0.004695785,0.009703852,0.0001548203,0.0000919016,0.004308098],"study_design_scores_gemma":[0.000002695544,0.00002518155,0.9934605,0.000003499799,0.00002430436,0.00003052222,0.0004084281,0.005514721,0.0004014186,0.0000236221,0.00009959058,0.000005528382],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999603,0.00001139241,0.00009910494,0.000008257268,5.206699e-7,0.000002240992,0.00004307442,0.000002958676,0.0002295068],"genre_scores_gemma":[0.9997502,0.00001172446,0.0001377409,0.000001932385,6.537346e-7,0.000004423661,0.00005288468,0.000001459273,0.00003910711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02555968,"threshold_uncertainty_score":0.05082178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01996817883952112,"score_gpt":0.277622510703375,"score_spread":0.2576543318638539,"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."}}