{"id":"W2774400402","doi":"","title":"Agriculture, nutrition and gender in India","year":2016,"lang":"en","type":"article","venue":"OpenDocs (Institute of Development Studies)","topic":"Agricultural Economics and Practices","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Multiple Sclerosis Scientific Research Foundation","keywords":"Agriculture; Business; Economic growth; Geography; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002044425,0.0001357056,0.0002356695,0.00001494491,0.0001295098,0.00002075713,0.0001270072,0.00004976083,0.00005725486],"category_scores_gemma":[0.00004634802,0.00003833499,0.00002633902,0.0001575001,0.00007034752,0.0004984157,0.0001428942,0.00004863802,0.00001961409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005188888,"about_ca_system_score_gemma":0.00001233394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003246214,"about_ca_topic_score_gemma":0.0005749364,"domain_scores_codex":[0.9991158,0.00002159967,0.0003167352,0.0002532433,0.0001043072,0.0001883204],"domain_scores_gemma":[0.9995975,0.000101973,0.0001600922,0.0000274748,0.0000608989,0.00005199113],"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":[0.0001671989,0.0007184204,0.1349201,0.0002328042,0.0004972974,0.00007182266,0.002430442,0.000002864642,0.2103066,0.01643734,0.01008392,0.6241312],"study_design_scores_gemma":[0.0005074246,0.00003901031,0.6690134,0.0001126207,0.00000797814,0.00001188562,0.00048296,2.130449e-7,0.005218223,0.0007101294,0.3236851,0.0002110728],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934912,0.001240613,9.870374e-7,0.001145072,0.0001733658,0.0002878229,0.00001088239,0.00001202267,0.003637981],"genre_scores_gemma":[0.9938182,0.004966854,0.0006855056,0.00008825059,0.00006658812,0.00005006578,0.00001899953,5.125944e-7,0.0003050495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6239201,"threshold_uncertainty_score":0.1563256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06021979008125673,"score_gpt":0.267345061500353,"score_spread":0.2071252714190963,"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."}}