{"id":"W2995171587","doi":"10.3390/w11122634","title":"Bridging the Data Gap in the Water Scarcity Footprint by Using Crop-Specific AWARE Factors","year":2019,"lang":"en","type":"article","venue":"Water","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Hydro-Québec; Danone; L'Oreal USA","keywords":"Water scarcity; Scarcity; Water use; Environmental science; Water resource management; Footprint; Agricultural engineering; Proxy (statistics); Water resources; Geography; Engineering; Agronomy; Mathematics; Economics; Ecology; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007660538,0.0001615073,0.000117228,0.00001240978,0.0001954915,0.0001202737,0.0009052388,0.00004913788,0.002269765],"category_scores_gemma":[0.000004307502,0.00005604103,0.00004440011,0.00005126392,0.0002412,0.0003135771,0.001146299,0.0002239933,0.0004595775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002666938,"about_ca_system_score_gemma":0.000002100453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001952273,"about_ca_topic_score_gemma":0.00007922507,"domain_scores_codex":[0.9984215,0.0001654458,0.0001801129,0.000391165,0.0003294098,0.0005123784],"domain_scores_gemma":[0.9987787,0.00002281674,0.00001975263,0.001131347,0.000001602921,0.00004575159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001131545,0.000075035,0.9187356,0.000007239703,0.000004397078,0.000004876366,0.005231802,0.0007098184,0.07322367,0.000001472568,0.0007820855,0.001212723],"study_design_scores_gemma":[0.0004211593,0.00004511352,0.8037887,0.0000096773,0.00001350055,0.00001296707,0.004021452,0.00333721,0.1238451,0.0003170505,0.06375132,0.0004367428],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976423,0.00001129094,0.00007459718,0.001078779,0.00009024119,0.0004082377,0.00001404514,0.00001191629,0.0006686061],"genre_scores_gemma":[0.9991471,0.00000419125,0.00001410608,0.0002383931,0.00002399874,0.000003872227,0.00006324832,0.000014065,0.0004910603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1149469,"threshold_uncertainty_score":0.9986423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04548162963578899,"score_gpt":0.2590807756351645,"score_spread":0.2135991459993755,"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."}}