{"id":"W6991397592","doi":"","title":"GEOGRAPHIC INFORMATION SYSTEMS FOR ASSESSMENT OF CLIMATE CHANGE EFFECTS\\nON TEFF IN ETHIOPIA","year":2014,"lang":"en","type":"other","venue":"Bioline International (Bioline International)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Climate change; Geographic information system; Information system; Geospatial analysis; Field (mathematics); Work (physics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001482037,0.0002899866,0.0001915903,0.00377314,0.000243228,0.001275764,0.0005929609,0.0002140816,0.01303656],"category_scores_gemma":[0.004184612,0.0001632638,0.0003050751,0.00610767,0.0001154277,0.0008656424,0.0007281452,0.000241038,0.001907504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001557305,"about_ca_system_score_gemma":0.003364455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1099799,"about_ca_topic_score_gemma":0.09642448,"domain_scores_codex":[0.9994572,0.0002044047,0.00006044193,0.00004670666,0.0001409154,0.00009036],"domain_scores_gemma":[0.9987379,0.0003430646,0.0001985286,0.0001102701,0.0004726527,0.0001376478],"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":[0.0005442481,0.0002746868,0.2877922,0.002367206,0.0003679525,0.0006448201,0.001663709,0.01784828,0.001809667,0.03363556,0.2329865,0.4200652],"study_design_scores_gemma":[0.000191317,0.0001210074,0.4576071,0.003225988,0.0003609658,0.0004456606,0.008947982,0.02213546,0.005424462,0.007429331,0.4940092,0.0001015088],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2576182,0.004066551,0.01688936,0.003273896,0.0003166278,0.001520342,0.5224208,0.001233818,0.1926603],"genre_scores_gemma":[0.6887513,0.006095867,0.06183342,0.0003116834,0.0000818684,0.001450283,0.205732,0.0003720503,0.03537158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1099799,"threshold_uncertainty_score":0.2186794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834437541851931,"score_gpt":0.3215602203818292,"score_spread":0.3032158449633099,"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."}}