{"id":"W2116772792","doi":"10.19030/jbcs.v9i3.7794","title":"Cranberries Of Wisconsin: Analyzing The Economic Impact","year":2013,"lang":"en","type":"article","venue":"Journal of Business Case Studies (JBCS)","topic":"Berry genetics and cultivation research","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic impact analysis; Economics; Business; Microeconomics","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":[0.0004884865,0.0002118991,0.0001696469,0.00202311,0.0009318237,0.001872213,0.0004049866,0.0003699679,0.003396169],"category_scores_gemma":[0.002629962,0.0001259627,0.0002604488,0.004239473,0.000508611,0.0006390205,0.0008815575,0.000505511,0.0002533517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004124865,"about_ca_system_score_gemma":0.00138997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3203794,"about_ca_topic_score_gemma":0.4817195,"domain_scores_codex":[0.9996517,0.00008912614,0.00001450704,0.00003285666,0.00009080931,0.0001209826],"domain_scores_gemma":[0.9986944,0.0004009846,0.0003337268,0.00003471717,0.0002688735,0.0002673985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001502049,0.0002438483,0.9779813,0.00005852537,0.0001303707,0.0007698993,0.002045107,0.001385161,0.0005344285,0.002308553,0.002580618,0.01181197],"study_design_scores_gemma":[0.000004997886,0.00007106434,0.9776224,0.00004278346,0.00005603186,0.00007188214,0.01355331,0.0009215183,0.0001732357,0.0001237853,0.007351362,0.000007659768],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947968,0.0002208626,0.00004105435,0.0001467596,0.000003466818,0.00001293398,0.0005732789,0.000001427966,0.004203482],"genre_scores_gemma":[0.9962792,0.0005529837,0.0001070973,0.00003128614,0.000006535704,0.00001668769,0.00124028,0.000001847505,0.001764018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3203794,"threshold_uncertainty_score":0.6370292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05117943361344263,"score_gpt":0.3092908809438524,"score_spread":0.2581114473304098,"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."}}