{"id":"W4412958154","doi":"10.1016/j.ecolind.2025.113982","title":"Bayesian multistage factorial analysis for unveiling multi-indicator effects on synergistic carrying capacity of water resource, environment and ecology: A case study of Ordos","year":2025,"lang":"en","type":"article","venue":"Ecological Indicators","topic":"Water Resources and Sustainability","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China; Foundation for Innovative Research Groups of the National Natural Science Foundation of China; National Natural Science Foundation of China","keywords":"Carrying capacity; Environmental science; Factorial analysis; Ecology; Resource (disambiguation); Bayesian probability; Computer science; Mathematics; Biology; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0007443043,0.0002474663,0.0006530027,0.0003024452,0.0003057546,0.00001574196,0.0002340346,0.0002261418,0.0002131813],"category_scores_gemma":[0.0002446581,0.0001671145,0.0001725566,0.0003230317,0.0004744965,0.00003612547,0.0003640731,0.0002125301,0.000002252425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004527483,"about_ca_system_score_gemma":0.00000863956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007326282,"about_ca_topic_score_gemma":0.0004997388,"domain_scores_codex":[0.9976943,0.0003975831,0.0005707885,0.0006766148,0.0002319582,0.0004288099],"domain_scores_gemma":[0.9984522,0.0007879027,0.0002134228,0.0003758919,0.000005199492,0.0001653428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001210978,0.001616852,0.9892776,0.0001001212,0.0003316351,0.0001587434,0.003226284,0.003575069,0.001204583,0.00002534934,0.000006923827,0.000355724],"study_design_scores_gemma":[0.003065928,0.002218179,0.9728451,0.000008008879,0.0008754563,0.000004154741,0.004924101,0.003848165,0.01120227,0.000171589,0.000497602,0.000339395],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952914,0.000003791807,0.002923239,0.00003611242,0.00006461937,0.001593102,0.0000282109,0.00002114219,0.00003835636],"genre_scores_gemma":[0.9993212,6.460778e-7,0.0003942839,0.00003497774,0.00001479635,0.000171956,0.000007479327,0.000009798271,0.0000449015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01643247,"threshold_uncertainty_score":0.6814732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01292560518795289,"score_gpt":0.2505966340952345,"score_spread":0.2376710289072816,"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."}}