{"id":"W7114892670","doi":"10.3390/mca30060136","title":"Integrating Emotion-Specific Factors into the Dynamics of Biosocial and Ecological Systems: Mathematical Modeling Approaches Accounting for Psychological Effects","year":2025,"lang":"en","type":"article","venue":"Mathematical and Computational Applications","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Biosocial theory; Perception; Population; Cognition; Toolbox; Ecological psychology; Affect (linguistics); Behavioural sciences","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001303584,0.001422761,0.001127297,0.001012072,0.0006819928,0.00232859,0.00171534,0.00189932,0.002576988],"category_scores_gemma":[0.003717561,0.0006165205,0.002072083,0.0006977893,0.002210637,0.003373491,0.001969037,0.002531523,0.0005134565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001325118,"about_ca_system_score_gemma":0.001216812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005375948,"about_ca_topic_score_gemma":0.002644648,"domain_scores_codex":[0.9996235,0.0001761729,0.00002097548,0.00006787051,0.00006360146,0.00004777483],"domain_scores_gemma":[0.998852,0.0006646814,0.0002056098,0.00008439169,0.000109933,0.00008336134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001280734,0.00004574454,0.002100129,0.0001926913,0.0001028542,0.0001933549,0.0004892474,0.3675982,0.0014294,0.6190087,0.001408156,0.00741873],"study_design_scores_gemma":[0.000009979149,0.00002682395,0.0008347778,0.0000627887,0.00004567139,0.00008480343,0.0001010657,0.6930208,0.000113998,0.3013109,0.004350491,0.00003793936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03676102,0.005486963,0.9208428,0.006334012,0.0003811852,0.00008819281,0.0003702549,0.0001711866,0.02956442],"genre_scores_gemma":[0.8213443,0.01765466,0.1365283,0.001948324,0.001318633,0.0009738306,0.0003960487,0.0002646092,0.01957128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005375948,"threshold_uncertainty_score":0.01068932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.106305029157375,"score_gpt":0.3276469569619419,"score_spread":0.2213419278045669,"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."}}