{"id":"W3151295882","doi":"10.29173/iasl7822","title":"Building Interest in Agricultural Research Through User Education Activities","year":2021,"lang":"en","type":"article","venue":"IASL Annual Conference Proceedings","topic":"Information Retrieval and Data Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Agency (philosophy); Indonesian; Agricultural education; Field (mathematics); Business; Agricultural communication; Funding Agency; Knowledge management; Public relations; Economic growth; Engineering; Political science; Computer science; Sociology; Geography; Social science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.01445469,0.0003497832,0.0004913248,0.003120995,0.002531651,0.007740122,0.001377299,0.001523709,0.01627974],"category_scores_gemma":[0.01605682,0.0003126939,0.0004571982,0.001993483,0.001834077,0.005342091,0.009164945,0.001747565,0.007187283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001244675,"about_ca_system_score_gemma":0.0026894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004917678,"about_ca_topic_score_gemma":0.0009406772,"domain_scores_codex":[0.9886093,0.007927101,0.0004001502,0.0006207026,0.001618891,0.0008238095],"domain_scores_gemma":[0.9695786,0.01711613,0.001706238,0.002711769,0.002869174,0.006017976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00022308,0.002697947,0.04578461,0.0007644591,0.00003460766,0.0008645089,0.1801715,0.0001643989,0.006621319,0.01181881,0.0245772,0.7262775],"study_design_scores_gemma":[0.00006650169,0.001043196,0.03237688,0.0006675248,0.00004807602,0.002033955,0.1024705,0.0009574718,0.005330826,0.008356555,0.8465324,0.0001159653],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5455013,0.004562904,0.04404298,0.01706837,0.0003340666,0.0007919548,0.000317225,0.002510637,0.3848706],"genre_scores_gemma":[0.9039404,0.003591273,0.02898873,0.002973566,0.0002469681,0.0004475151,0.0003089426,0.0002327355,0.0592697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01627974,"threshold_uncertainty_score":0.07644463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1013948748897259,"score_gpt":0.3529482636364138,"score_spread":0.2515533887466879,"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."}}