{"id":"W4206278518","doi":"10.36871/ek.up.p.r.2021.12.04.007","title":"IMPROVING THE DIGITALIZATION OF AGRICULTURE BASED ON THE CANADIAN EXPERIENCE","year":2021,"lang":"en","type":"article","venue":"EKONOMIKA I UPRAVLENIE PROBLEMY RESHENIYA","topic":"Digitalization and Economic Development in Agriculture","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Research Object; State (computer science); Digital transformation; Business; Similarity (geometry); Agricultural economics; Object (grammar); Geography; Regional science; Environmental resource management; Political science; Computer science; Economics","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":[0.001754201,0.0002170279,0.0001665958,0.002271934,0.00740197,0.005577315,0.0004845335,0.00039627,0.003547092],"category_scores_gemma":[0.00311721,0.000110109,0.0001378017,0.005942895,0.003051797,0.001875719,0.002206298,0.001094403,0.0001093312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07124046,"about_ca_system_score_gemma":0.0786717,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9813073,"about_ca_topic_score_gemma":0.9915489,"domain_scores_codex":[0.9982793,0.0003031196,0.00005619022,0.0001258213,0.0006292733,0.0006062837],"domain_scores_gemma":[0.9976684,0.0005903969,0.0001295656,0.00008199913,0.001016434,0.0005131828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001747965,0.0002142642,0.08602221,0.0008252966,0.00003627551,0.001663039,0.2822464,0.002279124,0.003510668,0.2162302,0.02647666,0.3803212],"study_design_scores_gemma":[0.00001060276,0.00006703264,0.1248793,0.0005443126,0.00003570383,0.000370885,0.178078,0.0007760525,0.001492042,0.00340115,0.6902799,0.00006497584],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6683199,0.01195754,0.00116704,0.02569514,0.0001183077,0.00008071292,0.0004514763,0.00004267675,0.2921672],"genre_scores_gemma":[0.9765928,0.01109868,0.0009343856,0.0005013224,0.00001079613,0.000007711405,0.00007796403,0.00001002899,0.01076623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07124046,"threshold_uncertainty_score":0.5168881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01504029290080124,"score_gpt":0.1781371152099077,"score_spread":0.1630968223091064,"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."}}