{"id":"W4416447709","doi":"10.1142/s0219525925500158","title":"RESILIENT FOOD-BIODIVERSITY OUTCOMES VIA STOCHASTIC CONTROL OF MULTIPLEX SOCIO-ECOLOGICAL NETWORKS UNDER WATER STRESS","year":2025,"lang":"en","type":"article","venue":"Advances in Complex Systems","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Resilience (materials science); Operationalization; Water scarcity; Adaptability; Psychological resilience; Sustainability; Correctness; Corporate governance; Hexapod","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":[0.000328829,0.0002028188,0.0004983551,0.00006158045,0.0001714652,0.00002214642,0.0004807881,0.0001190713,0.0001337569],"category_scores_gemma":[0.00002268521,0.0001379576,0.0000972905,0.0001746334,0.0003336747,0.0001618013,0.0002916287,0.0001525565,0.00003658993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002856824,"about_ca_system_score_gemma":0.000006597066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004199979,"about_ca_topic_score_gemma":0.001836502,"domain_scores_codex":[0.9981338,0.0001817299,0.0005279257,0.0004309148,0.0002881924,0.0004374344],"domain_scores_gemma":[0.9991143,0.0003266676,0.000155815,0.000315574,0.00002131577,0.00006634206],"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":[0.00002021272,0.00009806621,0.3906785,0.00004365968,0.00001649852,0.000002285049,0.00004601192,0.6083573,0.0001642414,0.0004281051,0.00003149448,0.0001136501],"study_design_scores_gemma":[0.001390068,0.0001422414,0.4168461,0.00009184896,0.00002157286,0.000002529006,0.0007808817,0.5793655,0.00002093231,0.0005979909,0.0004802766,0.0002600912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7410938,0.0008575375,0.2544407,0.0001662014,0.0009455053,0.001052455,0.00008610335,0.0000436275,0.001314059],"genre_scores_gemma":[0.9995976,0.00002195746,0.00007911101,0.0001006452,0.0000124901,0.00003672297,0.00001536249,0.000004893912,0.0001311895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2585039,"threshold_uncertainty_score":0.5625748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008966638583442673,"score_gpt":0.2382098438739488,"score_spread":0.2292432052905061,"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."}}