{"id":"W4408306332","doi":"10.1016/j.ecolind.2025.113289","title":"Spatio-Temporal evolution and scenario-based optimization of urban ecosystem services supply and Demand: A block-scale study in Xiamen, China","year":2025,"lang":"en","type":"article","venue":"Ecological Indicators","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Key Technologies Research and Development Program; Fujian Provincial Federation of Social Sciences; University of British Columbia; National Key Research and Development Program of China; China Scholarship Council","keywords":"Ecosystem services; China; Block (permutation group theory); Scale (ratio); Supply and demand; Ecosystem; Environmental resource management; Environmental science; Ecology; Geography; Economics; Mathematics; Cartography","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":[0.0006172439,0.0004908567,0.0003674764,0.000601517,0.0004021822,0.0007068113,0.0007762944,0.0005192509,0.00113235],"category_scores_gemma":[0.0007364239,0.0003124382,0.0006856582,0.000895198,0.0004168659,0.0005827975,0.0006243218,0.0003308743,0.00009456951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003394453,"about_ca_system_score_gemma":0.00146631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1525485,"about_ca_topic_score_gemma":0.1597188,"domain_scores_codex":[0.9997959,0.00006018024,0.00001218668,0.00004385782,0.00002346293,0.00006446092],"domain_scores_gemma":[0.9996442,0.0001137469,0.00006164097,0.00004630873,0.00006124558,0.00007287144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003773149,0.000680599,0.3723535,0.0001130493,0.000390298,0.001546063,0.0005490961,0.6032417,0.004185344,0.002625386,0.001891022,0.01204656],"study_design_scores_gemma":[0.00003573459,0.0001173846,0.2343887,0.000009391069,0.00008276946,0.00004831861,0.0009091293,0.7623759,0.0007393876,0.0004588038,0.0007985772,0.00003577964],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990966,0.00001849063,0.0002799794,0.00004223414,0.000001752701,0.00001163705,0.0002543508,0.00001035177,0.0002846883],"genre_scores_gemma":[0.9986644,0.00002770795,0.000478205,0.000009316785,0.000001296509,0.00001855532,0.0004684351,0.000003857954,0.0003282705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1525485,"threshold_uncertainty_score":0.3033211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003659658066210339,"score_gpt":0.2044129228857341,"score_spread":0.2007532648195238,"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."}}