{"id":"W2112931751","doi":"10.5194/hess-19-785-2015","title":"Reimagining the past – use of counterfactual trajectories in socio-hydrological modelling: the case of Chennai, India","year":2015,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Water resources management and optimization","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Stanford Woods Institute for the Environment; International Development Research Centre","keywords":"Counterfactual thinking; Futures contract; Scenario planning; Trajectory; Time horizon; Corporate governance; Resource (disambiguation); Endowment; Economics; Natural resource economics; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003576081,0.0003818235,0.0004525546,0.000910642,0.001323568,0.002900321,0.002133638,0.001818587,0.001860559],"category_scores_gemma":[0.01254486,0.0004018024,0.0009427426,0.002299512,0.002842793,0.002286427,0.002309653,0.0021019,0.0001395486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005537404,"about_ca_system_score_gemma":0.002732388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2359322,"about_ca_topic_score_gemma":0.1998329,"domain_scores_codex":[0.9983955,0.001219155,0.00006715117,0.0001246094,0.0001068461,0.0000866933],"domain_scores_gemma":[0.9857012,0.01122598,0.0006794449,0.001323969,0.000799761,0.0002696629],"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":[0.0001254715,0.00007338269,0.01789355,0.00006780482,0.00008710406,0.0006715771,0.001145759,0.932811,0.0001415399,0.04126891,0.0009408063,0.004773119],"study_design_scores_gemma":[0.00003338852,0.00004287106,0.004486086,0.00003118659,0.00004009578,0.00007138451,0.0008468717,0.9815345,0.0002383217,0.009358915,0.003261524,0.00005481039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9450905,0.0005064146,0.03381265,0.004506888,0.0001105379,0.0001260858,0.001194425,0.0002240574,0.01442844],"genre_scores_gemma":[0.9916235,0.0001551159,0.007352008,0.00006450597,0.000008060367,0.00003777204,0.0001759871,0.00001903047,0.0005641527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2359322,"threshold_uncertainty_score":0.4691179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05243970445522401,"score_gpt":0.2198709659266581,"score_spread":0.1674312614714341,"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."}}