{"id":"W6922077991","doi":"10.1139/cjss-2014-017","title":"Improving the spatial resolution and ecostratification of crop yield estimates in Canada","year":2015,"lang":"en","type":"article","venue":"BioOne Complete (BioOne)","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Yield (engineering); Crop yield; Crop; Spatial variability; Spatial analysis; Crop insurance; Spatial ecology","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":[0.002168114,0.0003904685,0.0003899305,0.002783026,0.001003232,0.001337184,0.001522042,0.0001885571,0.001400232],"category_scores_gemma":[0.007809822,0.000282147,0.0004914733,0.007382534,0.000317004,0.00053098,0.001571713,0.0004657402,0.0003700204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01567974,"about_ca_system_score_gemma":0.0276603,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9923051,"about_ca_topic_score_gemma":0.9944854,"domain_scores_codex":[0.9981411,0.0001321525,0.0001386688,0.0003557618,0.0009638747,0.0002684622],"domain_scores_gemma":[0.9937448,0.000504882,0.0002956193,0.0004169036,0.004826229,0.0002116276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003176601,0.00009608138,0.514799,0.0004533872,0.0003617963,0.0003651855,0.00290747,0.02760464,0.006075963,0.003599487,0.02479342,0.4186259],"study_design_scores_gemma":[0.00002883272,0.00002933871,0.9097108,0.0001179159,0.00007859641,0.00006536509,0.001964876,0.0495741,0.003864911,0.0004854513,0.03399619,0.00008366879],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8952039,0.001349105,0.03560067,0.0007592324,0.00005314717,0.0002703985,0.0521815,0.001172228,0.01340971],"genre_scores_gemma":[0.8991602,0.0009103947,0.05343666,0.0001158695,0.00001440893,0.0001280625,0.0409939,0.0001706124,0.005069844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01567974,"threshold_uncertainty_score":0.1137651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6079582759089364,"score_gpt":0.310597447842465,"score_spread":0.2973608280664713,"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."}}