{"id":"W6894249038","doi":"10.5683/sp3/wxcn3k","title":"Replication data for: Growth and vegetable yield of Amaranthus hybridus L. as impacted by harvest methods in southern Ontario, Canada","year":2024,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Amaranth; Yield (engineering); Amaranthus hybridus; Replication (statistics); Pruning","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":[0.001883314,0.001261756,0.001391037,0.0025287,0.002375476,0.001805824,0.002833815,0.00112446,0.04301976],"category_scores_gemma":[0.01326551,0.0006258079,0.001526642,0.007594849,0.0006195463,0.0005797778,0.001290969,0.001063071,0.0154458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01212079,"about_ca_system_score_gemma":0.02787743,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9722243,"about_ca_topic_score_gemma":0.9853544,"domain_scores_codex":[0.9985211,0.0001484589,0.0001677018,0.0003149433,0.000536292,0.0003115159],"domain_scores_gemma":[0.9880961,0.001405936,0.0009787809,0.001298345,0.007586496,0.0006342069],"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":[0.0002448896,0.00002507954,0.01549111,0.001637306,0.0002393214,0.00005054546,0.0001950148,0.0006780849,0.0002070643,0.0006014986,0.975526,0.00510411],"study_design_scores_gemma":[0.0008743441,0.0000425248,0.1738498,0.001424855,0.0004326412,0.0000724724,0.000639018,0.0008363073,0.0004881665,0.0007118214,0.8205045,0.0001235171],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005514296,0.00008146306,0.00005178739,0.00006159061,0.00001444959,0.00002460564,0.9983597,0.00005908054,0.0007958903],"genre_scores_gemma":[0.005639441,0.0001700837,0.0004841214,0.00007491603,0.00001012687,0.000333181,0.9890513,0.00008100509,0.004155801],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04301976,"threshold_uncertainty_score":0.1439155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03786455475370255,"score_gpt":0.3282504769326089,"score_spread":0.2903859221789064,"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."}}