{"id":"W6941656677","doi":"10.1371/journal.pclm.0000259.g001","title":"Time series of crop yield from 1908–2017.","year":2023,"lang":"en","type":"other","venue":"Figshare","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Yield (engineering); Crop; Crop yield; Series (stratigraphy); Crop production","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":[0.0002663054,0.0004764879,0.0002649813,0.001524525,0.0002077937,0.0005880001,0.0004364958,0.00031136,0.007911003],"category_scores_gemma":[0.001341054,0.0001078756,0.0002432834,0.002558746,0.0001368834,0.0004923043,0.0006223966,0.000753935,0.006875581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007803626,"about_ca_system_score_gemma":0.0008521829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1801149,"about_ca_topic_score_gemma":0.2327543,"domain_scores_codex":[0.9997684,0.00001248767,0.00001970411,0.00005981914,0.0000873979,0.00005228057],"domain_scores_gemma":[0.9990333,0.00006500783,0.0001974766,0.00003134198,0.000585042,0.00008790888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003486692,0.00007135615,0.2100419,0.0004416908,0.0002141964,0.0002379151,0.0002327023,0.002742395,0.0007102125,0.002158273,0.7495412,0.03325947],"study_design_scores_gemma":[0.00003114685,0.00004856658,0.7293859,0.0001469311,0.00006854808,0.0001577613,0.0005049793,0.002363414,0.0005234442,0.0002593868,0.266469,0.00004088527],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.1204069,0.001167933,0.0007683057,0.001056881,0.0006317018,0.00002434685,0.8620934,0.0007399464,0.01311049],"genre_scores_gemma":[0.1829385,0.0008980188,0.0005522531,0.000212589,0.0002060992,0.00004308209,0.7997333,0.000123752,0.01529246],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1801149,"threshold_uncertainty_score":0.3581331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03582490230566233,"score_gpt":0.2165637691263054,"score_spread":0.1807388668206431,"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."}}