{"id":"W4415314343","doi":"10.9734/jsrr/2025/v31i103619","title":"Perceived Appropriateness of Information and Beneficiary Characteristics under Kisan Mobile Sandesh","year":2025,"lang":"en","type":"article","venue":"Journal of Scientific Research and Reports","topic":"Knowledge Management and Technology","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Beneficiary; Agriculture; Uttar pradesh; Perception; Schedule; Quarter (Canadian coin)","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":[0.0006949291,0.0001035332,0.0001665289,0.0005976056,0.0005951279,0.0006622233,0.0001821381,0.0002250598,0.00497503],"category_scores_gemma":[0.002635334,0.0001101371,0.0001216503,0.0005755246,0.0003274788,0.000413705,0.0006151918,0.0002904609,0.0004740796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003655264,"about_ca_system_score_gemma":0.0004691332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004380523,"about_ca_topic_score_gemma":0.00878301,"domain_scores_codex":[0.9995572,0.0001216084,0.00005208224,0.00003430421,0.0001322418,0.0001025746],"domain_scores_gemma":[0.9980937,0.0005074727,0.0007135077,0.0000648875,0.000198277,0.0004222022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001371552,0.0003060213,0.975584,0.0001039372,0.00001544889,0.000624468,0.00825929,0.00006238303,0.002127328,0.0001900951,0.0002422287,0.01234761],"study_design_scores_gemma":[0.00000507279,0.0005164777,0.9777189,0.00002825268,0.00001130466,0.0003899484,0.01975181,0.0001226573,0.0001889953,0.00005432143,0.001204094,0.000008196767],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99903,0.00002749602,0.00001553986,0.00005154496,9.336514e-7,0.00001439502,0.00005951637,0.000001167004,0.0007994529],"genre_scores_gemma":[0.999189,0.00007894621,0.0000859055,0.00002701201,0.000002481533,0.00001198045,0.00006409566,6.320565e-7,0.0005400314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00497503,"threshold_uncertainty_score":0.01664311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08434384432707744,"score_gpt":0.4097873072727456,"score_spread":0.3254434629456682,"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."}}