{"id":"W4410601083","doi":"10.1007/s00267-025-02183-5","title":"Integrating Social Attitudes and Behaviors with Biophysical and Socioeconomic Factors for Enhanced Watershed Planning","year":2025,"lang":"en","type":"article","venue":"Environmental Management","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Socioeconomic status; Watershed; Forest management; Nature Conservation; Geography; Environmental resource management; Environmental planning; Psychology; Environmental science; Ecology; Sociology; Forestry; Computer science; Biology; Demography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001745196,0.0002911185,0.0002594243,0.0008228434,0.0006275301,0.001974134,0.0004137539,0.0004565037,0.004704593],"category_scores_gemma":[0.005688609,0.0001681678,0.0004063922,0.0007714306,0.0004511284,0.00107295,0.001027142,0.0006580402,0.0002147042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00119755,"about_ca_system_score_gemma":0.003172108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01532036,"about_ca_topic_score_gemma":0.07508983,"domain_scores_codex":[0.9991641,0.000486158,0.00004202769,0.00005582855,0.0001779282,0.00007396271],"domain_scores_gemma":[0.9980075,0.0009823828,0.0002991101,0.00007637005,0.0002860701,0.0003485707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002157073,0.005952255,0.5876136,0.0002591508,0.0004959151,0.0001732457,0.001696093,0.01937798,0.002860754,0.005500434,0.002155682,0.3736992],"study_design_scores_gemma":[0.00008104891,0.001069951,0.8702407,0.0004186515,0.0007246882,0.0001659931,0.009567614,0.08051205,0.003443856,0.0241197,0.009546482,0.0001093118],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9486785,0.0004662065,0.02401682,0.004808582,0.00007661311,0.0002113764,0.0003560359,0.0001199,0.02126603],"genre_scores_gemma":[0.9786012,0.0002772596,0.0199008,0.0001300696,0.00001988615,0.0000688037,0.00009066147,0.00000993236,0.0009013606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01532036,"threshold_uncertainty_score":0.03046238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005679698914553013,"score_gpt":0.2213122575912026,"score_spread":0.2156325586766496,"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."}}