{"id":"W7067165665","doi":"","title":"Learning to ADAPT: monitoring and evaluation approaches in climate change adaptation and disaster risk reduction – challenges, gaps and ways forward","year":2013,"lang":"en","type":"other","venue":"OpenDocs (Institute of Development Studies)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department for International Development; Department for Environment, Food and Rural Affairs, UK Government; International Development Research Centre; Global Environment Facility","keywords":"Disaster risk reduction; Vulnerability (computing); Climate change; Adaptation (eye); Context (archaeology); Climate change adaptation; Set (abstract data type); Risk management","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001770386,0.0006300914,0.0009209357,0.0008175576,0.0002950884,0.00007294984,0.0001247562,0.0002079319,0.00001732333],"category_scores_gemma":[0.0001889678,0.0006151019,0.00002782021,0.0002703602,0.0001937164,0.0006752973,0.0005997186,0.0003249931,0.00005279161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003613315,"about_ca_system_score_gemma":0.00008160666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004273987,"about_ca_topic_score_gemma":0.001437243,"domain_scores_codex":[0.9968457,0.00018825,0.0006924957,0.001047324,0.0007108367,0.0005154004],"domain_scores_gemma":[0.9986367,0.00003905793,0.0007875294,0.0002428926,0.0001437504,0.0001501344],"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":[0.0002169791,0.0001497391,0.006413817,0.002527677,0.001043298,0.000009690368,0.1111188,0.0005625266,0.00009379056,0.0005989348,0.002036517,0.8752283],"study_design_scores_gemma":[0.01330681,0.0009169154,0.1481562,0.02535666,0.001745693,0.0001144276,0.1614544,0.004644827,0.0005204675,0.000259677,0.6383367,0.005187274],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5523827,0.2898508,0.0002370339,0.0005932385,0.003899019,0.02325049,0.0001504752,0.0005119549,0.1291243],"genre_scores_gemma":[0.5811027,0.3401039,0.06036115,0.000008445805,0.0009060107,0.006253934,0.0003053445,0.001141906,0.009816592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.870041,"threshold_uncertainty_score":0.99963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2520797516688122,"score_gpt":0.3258161391673831,"score_spread":0.07373638749857092,"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."}}