{"id":"W2533049720","doi":"10.2166/wp.2016.004","title":"Comparative analysis of water rights entitlements in India and China","year":2016,"lang":"en","type":"article","venue":"Water Policy","topic":"Water resources management and optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"China; Water resources; Resource (disambiguation); Political science; Business; Environmental resource management; Environmental economics; Environmental planning; Economics; Geography; Computer science; Law; Ecology","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":[0.001515208,0.000164609,0.0002868377,0.003396649,0.0008807091,0.001405769,0.000480272,0.0001666156,0.001705151],"category_scores_gemma":[0.002564442,0.0000832676,0.0003820866,0.005955369,0.001306697,0.0008130357,0.001363481,0.0003262479,0.00008154786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005736583,"about_ca_system_score_gemma":0.003086977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07798769,"about_ca_topic_score_gemma":0.110321,"domain_scores_codex":[0.9983007,0.0004470946,0.0000817819,0.00009940141,0.0004697713,0.0006013459],"domain_scores_gemma":[0.9981086,0.0006713005,0.000426845,0.0001445176,0.0004715978,0.000177061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.001110473,0.0003054498,0.6406456,0.0004348427,0.000630598,0.002216418,0.0238069,0.0202831,0.009146016,0.1588241,0.00240566,0.1401909],"study_design_scores_gemma":[0.00001574644,0.000169225,0.97384,0.00003019247,0.0001409793,0.00009745301,0.01141986,0.003529598,0.001645639,0.001823683,0.007256753,0.00003101816],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870735,0.0001092653,0.0001253461,0.00007058959,0.0000015038,0.000009866446,0.00006968613,0.000004123291,0.01253616],"genre_scores_gemma":[0.9992142,0.00005418315,0.00005967165,0.000006388444,8.255792e-7,0.000006374275,0.0000628363,0.000001228284,0.0005944304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07798769,"threshold_uncertainty_score":0.1550674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006504602059913188,"score_gpt":0.2212057319118315,"score_spread":0.2147011298519183,"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."}}