{"id":"W6982761851","doi":"","title":"Kraft Heinz lays off 200 white-collar workers in U.S., Canada","year":2017,"lang":"en","type":"other","venue":"Internet Archive (Internet Archive)","topic":"Probability and Statistical Research","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vice president; Government (linguistics); Kraft paper; Heinz body","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":[0.0005745331,0.0008080852,0.0005218073,0.002123998,0.009901728,0.0035507,0.0006825624,0.001284552,0.1563324],"category_scores_gemma":[0.00135785,0.0004635672,0.0003617007,0.001738633,0.001153336,0.0007863123,0.001664855,0.001549524,0.04339982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01570434,"about_ca_system_score_gemma":0.03214994,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9106004,"about_ca_topic_score_gemma":0.9705158,"domain_scores_codex":[0.998941,0.00002092512,0.00001681455,0.00009215443,0.0005297461,0.0003993066],"domain_scores_gemma":[0.9982413,0.0000901153,0.00003881282,0.00005102719,0.0008581802,0.0007205062],"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":[0.00003010536,0.00002153475,0.0019597,0.00001952618,0.000003068215,0.00009096626,0.0001221214,0.00006399281,0.0001730928,0.003027925,0.9657211,0.02876672],"study_design_scores_gemma":[0.000007312992,0.00001229498,0.008910433,0.00007595253,0.000003944992,0.00003694503,0.0007699922,0.0001748729,0.0004046377,0.0004667572,0.9891211,0.00001571364],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02671843,0.007170169,0.0008340942,0.03085559,0.00419028,0.0001767402,0.01153442,0.0008372854,0.9176829],"genre_scores_gemma":[0.01397602,0.001462573,0.0001667114,0.001125966,0.0001091612,0.00001166177,0.001022438,0.0001126775,0.9820128],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9106004,"threshold_uncertainty_score":0.5229841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03228851553609137,"score_gpt":0.2984158784517144,"score_spread":0.266127362915623,"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."}}