{"id":"W4410768575","doi":"10.32628/ijsrst2512367","title":"AI-Driven Water Resource Management in Tourism-Intensive Regions: A Smart Sustainability Model","year":2025,"lang":"en","type":"article","venue":"International Journal of Scientific Research in Science and Technology","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regent College","funders":"","keywords":"Tourism; Sustainability; Business; Resource (disambiguation); Environmental economics; Environmental resource management; Computer science; Environmental science; Geography; Economics; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005969562,0.0001011146,0.000174522,0.003926711,0.0003227639,0.0002011774,0.001833259,0.00007346051,0.00001385808],"category_scores_gemma":[0.0007624038,0.0000733722,0.00002980244,0.003241823,0.006264534,0.0006258182,0.002669736,0.0005036599,0.00000782926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001479828,"about_ca_system_score_gemma":0.000196627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001304768,"about_ca_topic_score_gemma":0.0003595972,"domain_scores_codex":[0.9965403,0.00008907606,0.0004687975,0.0005472302,0.001727779,0.0006268164],"domain_scores_gemma":[0.9980139,0.0000729785,0.00007125484,0.0003181267,0.001448623,0.0000751129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006948109,0.001347511,0.08019985,0.00007215865,0.0001525837,0.003362448,0.00498681,0.01268351,0.05991914,0.6185064,0.07781332,0.1402614],"study_design_scores_gemma":[0.0006226056,0.00008566247,0.002810133,0.0001289369,0.000003183411,0.00007011474,0.00686618,0.01260698,0.009478886,0.95184,0.01538082,0.0001065328],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.812255,0.0001040062,0.0005926781,0.1595227,0.0004928546,0.0003316034,0.000002988455,0.00001705139,0.02668117],"genre_scores_gemma":[0.9972838,0.00002465062,0.0005064494,0.0002675297,0.000009131108,0.0000237561,4.210366e-7,0.000003366785,0.001880905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3333335,"threshold_uncertainty_score":0.9964399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0313730241580983,"score_gpt":0.3525004228153737,"score_spread":0.3211273986572755,"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."}}