{"id":"W4366529026","doi":"10.1142/s2382624x23400040","title":"Testing the Predictive Power of Hydro-Economic Supply-Side Input–Output Models Under Different Water Availability and Economic Conditions Over Time in a Transboundary River Basin","year":2023,"lang":"en","type":"article","venue":"Water Economics and Policy","topic":"Water resources management and optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Global Institute for Water Security; University of Saskatchewan","funders":"Global Water Futures","keywords":"Environmental science; Context (archaeology); Climate change; Water supply; Precipitation; Water balance; Predictive power; Water use; Economic model; Range (aeronautics); Economic impact analysis; Drainage basin; Water resource management; Hydrology (agriculture); Economics; Meteorology; Geography; Environmental engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001613219,0.0001903827,0.0002510811,0.000243534,0.0000791759,0.00009437922,0.0001035436,0.00006964577,0.00007682486],"category_scores_gemma":[0.000001395685,0.0001315418,0.00004617988,0.00002847755,0.0001545801,0.0002844971,0.0001088174,0.00008939129,0.00005922234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001514053,"about_ca_system_score_gemma":0.00001090251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005648886,"about_ca_topic_score_gemma":0.000128269,"domain_scores_codex":[0.9990566,0.00002927887,0.0003447562,0.0002402216,0.00002189928,0.0003072925],"domain_scores_gemma":[0.9996666,0.00005336993,0.0000341724,0.0001847235,0.000005185912,0.00005588887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002562983,0.00001259011,0.007883017,0.00004457462,0.00009800059,8.398712e-7,0.00595127,0.9845958,0.0003633883,0.0006474283,0.0001837928,0.0001936538],"study_design_scores_gemma":[0.0006563147,0.00003441846,0.07899609,0.00001472015,0.00002480805,0.000002292115,0.00005747002,0.9041238,0.001055115,0.01403625,0.0007900194,0.0002087343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997251,0.00001425823,0.00006600001,0.0005478305,0.00006687925,0.0002959124,0.00019871,0.00006096225,0.00149846],"genre_scores_gemma":[0.99927,0.0001081967,0.00001595188,0.00008592262,0.00006185933,0.00002920117,0.0001047928,0.00003483238,0.0002892185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08047205,"threshold_uncertainty_score":0.5364119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01143338906617173,"score_gpt":0.1939407600372649,"score_spread":0.1825073709710932,"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."}}