{"id":"W2524966155","doi":"10.1073/pnas.1614023113","title":"Big data has big potential for applications to climate change adaptation","year":2016,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Institute for Sustainable Development; McGill University","funders":"","keywords":"Big data; Adaptation (eye); Climate change; Data science; Climate change adaptation; Computer science; Biology; Ecology; Data mining; Neuroscience","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0148235,0.001748872,0.001706414,0.004221281,0.001522239,0.009926436,0.002761367,0.003833062,0.02023439],"category_scores_gemma":[0.08930063,0.001023735,0.00174402,0.009521082,0.00313351,0.01645906,0.008098961,0.006877075,0.005981087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001623956,"about_ca_system_score_gemma":0.003592609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002615539,"about_ca_topic_score_gemma":0.003257764,"domain_scores_codex":[0.9932911,0.003349419,0.0003916836,0.0008120917,0.001878356,0.0002772634],"domain_scores_gemma":[0.9183472,0.0554586,0.001695395,0.01250367,0.007214843,0.004780133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001208457,0.0001768803,0.01275752,0.002614286,0.0008753368,0.0005409105,0.0007801014,0.01336059,0.002142409,0.1813888,0.4338146,0.3503402],"study_design_scores_gemma":[0.0001169217,0.00006581809,0.002763206,0.0006922547,0.0001299491,0.0001187016,0.0006596283,0.02170818,0.0009657887,0.6873919,0.2852883,0.00009939798],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.02054411,0.07604459,0.2719434,0.4531341,0.02795151,0.0007865489,0.05413039,0.01689581,0.07856951],"genre_scores_gemma":[0.4089938,0.08430472,0.3620507,0.04795472,0.02918731,0.001784443,0.04986754,0.004239936,0.0116168],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02023439,"threshold_uncertainty_score":0.07839507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3373220624396064,"score_gpt":0.3403760567906283,"score_spread":0.003053994351021883,"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."}}