{"id":"W4249712158","doi":"10.1079/cabicomm-27-994","title":"Agricultural training for Pakistan’s rural women","year":2016,"lang":"en","type":"report","venue":"","topic":"Agricultural Economics and Practices","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Centre for International Agricultural Research; Agriculture and Agri-Food Canada; Ministry of Agriculture of the People's Republic of China; Department for International Development","keywords":"Training (meteorology); Agriculture; Geography; Business; Agricultural economics; Agroforestry; Environmental science; Economics; Archaeology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008055297,0.0004746582,0.0006753712,0.00001255333,0.0003506039,0.0002993287,0.0004830615,0.0004822644,0.004043565],"category_scores_gemma":[0.0001565665,0.0001075339,0.0004170621,0.000118172,0.00005097651,0.0003711191,0.000103384,0.0002253171,0.0001264744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004918062,"about_ca_system_score_gemma":0.000102377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003734724,"about_ca_topic_score_gemma":0.0009331501,"domain_scores_codex":[0.9976054,0.00004447815,0.0006193676,0.0005431004,0.000367943,0.0008196769],"domain_scores_gemma":[0.9977544,0.0008503363,0.0006358075,0.00007611717,0.0004218378,0.0002614818],"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":[0.0001079173,0.00006170931,0.000178305,0.0000716392,0.0003048846,0.000003807842,0.0004275144,5.044438e-7,0.02498301,0.001183694,0.2372175,0.7354595],"study_design_scores_gemma":[0.0001535672,0.0002725238,0.0123859,0.00006776164,0.00003380571,0.00004816575,0.004620298,8.620498e-7,0.00008536472,0.0006166436,0.9810756,0.0006395243],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6473846,0.0006606605,0.00000428051,0.006167251,0.002295604,0.001271092,0.001938144,0.000303647,0.3399747],"genre_scores_gemma":[0.7366713,0.002728024,0.0001963922,0.0004917266,0.01070768,0.0008333581,0.001927765,0.000007189953,0.2464366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.743858,"threshold_uncertainty_score":0.9968669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08100056675799529,"score_gpt":0.297995194920788,"score_spread":0.2169946281627927,"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."}}